@article {dunkelberger2023, title = {Hybrid FES-exoskeleton control: Using MPC to distribute actuation for elbow and wrist movements}, journal = {Frontiers in Neurorobotics}, volume = {17}, year = {2023}, pages = {1127783}, doi = {10.3389/fnbot.2023.1127783}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Frontiers_2023_Dunkelberger_hybridFESexo.pdf}, author = {Dunkelberger, Nathan and Berning, Jeffrey and Schearer, Eric M and O{\textquoteright}Malley, Marcia K} } @article {sciro_brown2023, title = {Touching reality: Bridging the user-researcher divide in upper-limb prosthetics}, journal = {Science Robotics}, volume = {8}, number = {83}, year = {2023}, pages = {eadk9421}, abstract = {

Realistically improving upper-limb prostheses is only possible if we listen to users{\textquoteright} actual technological needs. Realistically improving upper-limb prostheses is only possible if we listen to users{\textquoteright} actual technological needs.

}, doi = {10.1126/scirobotics.adk9421}, url = {https://www.science.org/doi/abs/10.1126/scirobotics.adk9421}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/SciRoboticsBrown2023_prosthesis_users.pdf}, author = {J. D. Brown and E. Battaglia and S. Engdahl and G. Levay and A. C. Parks and E. Skinner and M. K. O{\textquoteright}Malley} } @article {9788113, title = {Design, Characterization, and Dynamic Simulation of the MAHI Open Exoskeleton Upper Limb Robot}, journal = {IEEE/ASME Transactions on Mechatronics}, volume = {27}, number = {4}, year = {2022}, month = {06/2022}, pages = {1829-1836}, doi = {10.1109/TMECH.2022.3175507}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Dunkelberger_TMECH_AIM.pdf}, author = {Dunkelberger, Nathan and Berning, Jeffrey and Dix, Kevin J. and Ramirez, Samuel A. and O{\textquoteright}Malley, Marcia K.} } @article {legeza2022eval, title = {Evaluation of Robotic-Assisted Carotid Artery Stenting in a Virtual Model Using Motion-Based Performance Metrics}, journal = {Journal of Endovascular Therapy}, year = {2022}, pages = {15266028221125592}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Legaza_JET2022.pdf}, author = {Legeza, Peter T and Lettenberger, Ahalya B and Murali, Barathwaj and Johnson, Lianne R and Berczeli, Marton and Byrne, Michael D and Britz, Gavin and O{\textquoteright}Malley, Marcia K and Lumsden, Alan B} } @proceedings {2057, title = {Explorations of wrist haptic feedback for AR/VR interactions with Tasbi}, year = {2022}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Pezent_CHI_2022.pdf}, author = {Pezent, Evan and Gupta, Aakar and Duhaime, Hank and O{\textquoteright}Malley, Marcia and Israr, Ali and Samad, Majed and Robinson, Shea and Agarwal, Priyanshu and Benko, Hrvoje and Colonnese, Nick} } @article {2016, title = {Haptic feedback based on movement smoothness improves performance in a perceptual-motor task}, journal = {IEEE Transactions on Haptics}, volume = {15}, number = {2}, year = {2022}, month = {2022}, pages = {382-391}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/IEEE_ToH_2021_Sullivan_haptic-feedback-smoothness-MT5.pdf}, author = {Sullivan, Jennifer L and Pandey, Shivam and Byrne, Michael D and O{\textquoteright}Malley, Marcia K} } @article {IJRRLosey2021, title = {Physical interaction as communication: Learning robot objectives online from human corrections}, journal = {The International Journal of Robotics Research}, volume = {41}, number = {1}, year = {2022}, month = {Jan 2022}, pages = {02783649211050958}, chapter = {20-44}, abstract = {

When a robot performs a task next to a human, physical interaction is inevitable: the human might push, pull, twist, or guide the robot. The state of the art treats these interactions as disturbances that the robot should reject or avoid. At best, these robots respond safely while the human interacts; but after the human lets go, these robots simply return to their original behavior. We recognize that physical human{\textendash}robot interaction (pHRI) is often intentional: the human intervenes on purpose because the robot is not doing the task correctly. In this article, we argue that when pHRI is intentional it is also informative: the robot can leverage interactions to learn how it should complete the rest of its current task even after the person lets go. We formalize pHRI as a dynamical system, where the human has in mind an objective function they want the robot to optimize, but the robot does not get direct access to the parameters of this objective: they are internal to the human. Within our proposed framework human interactions become observations about the true objective. We introduce approximations to learn from and respond to pHRI in real-time. We recognize that not all human corrections are perfect: often users interact with the robot noisily, and so we improve the efficiency of robot learning from pHRI by reducing unintended learning. Finally, we conduct simulations and user studies on a robotic manipulator to compare our proposed approach with the state of the art. Our results indicate that learning from pHRI leads to better task performance and improved human satisfaction.

}, doi = {10.1177/02783649211050958}, url = {https://doi.org/10.1177/02783649211050958}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Losey_IJRR2021.pdf}, author = {Dylan P. Losey and Andrea Bajcsy and Marcia K. O{\textquoteright}Malley and Anca D. Dragan} } @inbook {2020, title = {A Textile-Based Approach to Wearable Haptic Devices}, booktitle = {2022 IEEE 5th International Conference on Soft Robotics (RoboSoft)}, year = {2022}, doi = {10.1109/RoboSoft54090.2022.9762149}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Jumet_RoboSoft2022.pdf}, author = {Jumet, Barclay and Zook, Zane A. and Xu, Doris and Fino, Nathaniel and Rajappan, Anoop and Schara, Mark W. and Berning, Jeffrey and Escobar, Nicolas and O{\textquoteright}Malley, Marcia K. and Preston, Daniel J.} } @proceedings {2012, title = {Comparing Manual and Robotic-Assisted Carotid Artery Stenting Using Motion-Based Performance Metrics}, year = {2021}, month = {2021}, pages = {1388-1391}, doi = {10.1109/EMBC46164.2021.9630895}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Lettenberger_EMBC_2021_motion-based-metrics-manual-robot.pdf}, author = {Lettenberger, Ahalya B. and Murali, Barathwaj and Legeza, Peter and Byrne, Michael D. and Lumsden, Alan B. and O{\textquoteright}Malley, Marcia K.} } @article {yousaf2021design, title = {Design and Characterization of a Passive Instrumented Hand}, journal = {ASME Letters in Dynamic Systems and Control}, volume = {1}, number = {1}, year = {2021}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/ALDSC-19-1082-1_Two_Col.pdf}, author = {Yousaf, Saad N and Joshi, Victoria S and Britt, John E and Rose, Chad G and O{\textquoteright}Malley, Marcia K} } @proceedings {britt2021emg, title = {Electromyographic Classification to Control the SPAR Glove}, volume = {54}, number = {20}, year = {2021}, pages = {244{\textendash}250}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Britt_MECC_IFAC_2021.pdf}, author = {Britt, John E and O{\textquoteright}Malley, Marcia K and Rose, Chad G} } @article {9141429, title = {A Multi-sensory Approach to Present Phonemes as Language through a Wearable Haptic Device}, journal = {IEEE Transactions on Haptics}, volume = {14}, number = {1}, year = {2021}, month = {Jan-Mar 2021}, pages = {188-199}, doi = {10.1109/TOH.2020.3009581}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/IEEE_ToH_2020_Dunkelberger_small.pdf}, author = {N. Dunkelberger and J. L. Sullivan and J. Bradley and I. Manickam and G. Dasarathy and R. G. Baraniuk and M. K. O{\textquoteright}Malley} } @article {Berning2021COBME, title = {Myoelectric Control and Neuromusculoskeletal Modeling: Complementary Technologies for Rehabilitation Robotics}, journal = {Current Opinion in Biomedical Engineering}, year = {2021}, pages = {100313}, abstract = {

Stroke and spinal cord injury (SCI) are a leading cause of disability in the United States, and researchers have pursued using robotic devices to aid rehabilitation efforts for resulting upper-extremity impairments. To date, however, robotic rehabilitation of the upper limb has produced only limited improvement in functional outcomes compared to traditional therapy. This paper explores the potential of myoelectric control and neuromusculoskeletal modeling for robotic rehabilitation using the current state of the art of each individual field as evidence. Continuing advances in the fields of myoelectric control and neuromusculoskeletal modeling offer opportunities for further improvements of rehabilitation robot control strategies. Specifically, personalized neuromusculoskeletal models driven by a subject{\textquoteright}s electromyography signals may provide accurate predictions of the subject{\textquoteright}s muscle forces and joint moments which, when used to design novel control strategies, could yield new approaches to robotic therapy for stroke and SCI that surpass the efficacy of traditional therapy.

}, keywords = {Electromyography, neuromusculoskeletal modeling, robotic rehabilitation, upper limb motor impairment}, issn = {2468-4511}, doi = {https://doi.org/10.1016/j.cobme.2021.100313}, url = {https://www.sciencedirect.com/science/article/pii/S2468451121000532}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/BerningCOBME2021_preprint.pdf}, author = {Jeffrey Berning and Gerard E. Francisco and Shuo-Hsiu Chang and Benjamin J. Fregly and Marcia K. O{\textquoteright}Malley} } @article {kadivar2021, title = {Single limb cable driven wearable robotic device for upper extremity movement support after traumatic brain injury}, journal = {Journal of Rehabilitation and Assistive Technologies Engineering}, volume = {8}, year = {2021}, pages = {20556683211002448}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/JRATE_2021_Kadivar_Armstrong.pdf}, author = {Kadivar, Zahra and Beck, Christopher E and Rovekamp, Roger N and O{\textquoteright}Malley, Marcia K} } @article {murali2021vel, title = {Velocity-Domain Motion Quality Measures for Surgical Performance Evaluation and Feedback}, journal = {Journal of Medical Devices}, volume = {15}, number = {1}, year = {2021}, pages = {011107}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/med_015_01_011107.pdf}, author = {Murali, Barathwaj and Belvroy, Viony M and Pandey, Shivam and Bismuth, Jean and Byrne, Michael D and O{\textquoteright}Malley, Marcia K} } @proceedings {1978, title = {Explorations of Wrist Haptic Feedback for AR/VR Interactions with Tasbi}, year = {2020}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, keywords = {bracelet, haptics, multisensory, Virtual reality, wearables}, isbn = {9781450368193}, doi = {10.1145/3334480.3383151}, url = {https://doi.org/10.1145/3334480.3383151}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/tasbi_chi2020_20200216_compressed.pdf}, author = {Pezent, Evan and O{\textquoteright}Malley, Marcia K. and Israr, Ali and Samad, Majed and Robinson, Shea and Agarwal, Priyanshu and Benko, Hrvoje and Colonnese, Nicholas} } @article {1969, title = {In the Fundamentals of Endovascular and Vascular Surgery model motion metrics reliably differentiate competency}, journal = {Journal of Vascular Surgery}, volume = {72}, number = {6}, year = {2020}, month = {12/2020}, pages = {2161-2165}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/JVS2020_Belvroy_et_al.pdf}, author = {Viony Belvroy and Barathwaj Murali and Malachi G. Sheahan and Marcia K. O{\textquoteright}Malley and Jean Bismuth} } @article {1961, title = {Multi-Sensory Stimuli Improve Distinguishability of Cutaneous Haptic Cues}, journal = {IEEE Transactions on Haptics}, volume = {13}, number = {2}, year = {2020}, month = {April-June 2020}, pages = {286-297}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Sullivan_ToH_2020_multi-sensory.pdf}, author = {Sullivan, Jennifer L and Dunkelberger, Nathan and Bradley, Joshua and Young, Joseph and Israr, Ali and Lau, Frances and Klumb, Keith and Abnousi, Freddy and O{\textquoteright}Malley, Marcia K} } @article {BHAGAT2020102502, title = {Neural activity modulations and motor recovery following brain-exoskeleton interface mediated stroke rehabilitation}, journal = {NeuroImage: Clinical}, volume = {28}, year = {2020}, pages = {102502}, abstract = {

Brain-machine interfaces (BMI) based on scalp EEG have the potential to promote cortical plasticity following stroke, which has been shown to improve motor recovery outcomes. However, the efficacy of BMI enabled robotic training for upper-limb recovery is seldom quantified using clinical, EEG-based, and kinematics-based metrics. Further, a movement related neural correlate that can predict the extent of motor recovery still remains elusive, which impedes the clinical translation of BMI-based stroke rehabilitation. To address above knowledge gaps, 10 chronic stroke individuals with stable baseline clinical scores were recruited to participate in 12 therapy sessions involving a BMI enabled powered exoskeleton for elbow training. On average, 132\ {\textpm}\ 22 repetitions were performed per participant, per session. BMI accuracy across all sessions and subjects was 79\ {\textpm}\ 18\% with a false positives rate of 23\ {\textpm}\ 20\%. Post-training clinical assessments found that FMA for upper extremity and ARAT scores significantly improved over baseline by 3.92\ {\textpm}\ 3.73 and 5.35\ {\textpm}\ 4.62 points, respectively. Also, 80\% participants (7 with moderate-mild impairment, 1 with severe impairment) achieved minimal clinically important difference (MCID: FMA-UE \>5.2 or ARAT \>5.7) during the course of the study. Kinematic measures indicate that, on average, participants{\textquoteright} movements became faster and smoother. Moreover, modulations in movement related cortical potentials, an EEG-based neural correlate measured contralateral to the impaired arm, were significantly correlated with ARAT scores (ρ\ =\ 0.72, p\ \<\ 0.05) and marginally correlated with FMA-UE (ρ\ =\ 0.63, p\ =\ 0.051). This suggests higher activation of ipsi-lesional hemisphere post-intervention or inhibition of competing contra-lesional hemisphere, which may be evidence of neuroplasticity and cortical reorganization following BMI mediated rehabilitation therapy.

}, keywords = {Brain-machine interface, Clinical trial, Exoskeletons, Movement related cortical potentials, stroke rehabilitation}, issn = {2213-1582}, doi = {https://doi.org/10.1016/j.nicl.2020.102502}, url = {http://www.sciencedirect.com/science/article/pii/S2213158220303399}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/NeuroImage_2020_Bhagat_BMI_EEG_exo.pdf}, author = {Nikunj A. Bhagat and Nuray Yozbatiran and Jennifer L. Sullivan and Ruta Paranjape and Colin Losey and Zachary Hernandez and Zafer Keser and Robert Grossman and Gerard E. Francisco and Marcia K. O{\textquoteright}Malley and Jose L. Contreras-Vidal} } @inbook {1979, title = {Simply Grasping Simple Shapes: Commanding a Humanoid Hand with a Shape-Based Synergy}, booktitle = {Robotics Research}, year = {2020}, pages = {541{\textendash}553}, publisher = {Springer}, organization = {Springer}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/farrell2020simply.pdf}, author = {Farrell, Logan C and Dennis, Troy A and Badger, Julia and O{\textquoteright}Malley, Marcia K} } @proceedings {1990, title = {Towards Automated Performance Assessment using Velocity-based Motion Quality Metrics}, year = {2020}, month = {11/2020}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/ISMR_2020_Murali_et_al_FinalVersion_0.pdf}, author = {Barathwaj Murali and Viony Belvroy and Shivam Pandey and Michael D. Byrne and Jean Bismuth and Marcia K. O{\textquoteright}Malley} } @proceedings {1962, title = {Design and Characterization of a Passive Instrumented Hand}, year = {2019}, month = {10/2019}, doi = {https://doi.org/10.1115/DSCC2019-9082}, url = {https://asmedigitalcollection.asme.org/DSCC/proceedings/DSCC2019/59148/V001T05A007/1070466?searchresult=1}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Instrumented_Hand_DSCC_Revised-compressed.pdf}, author = {Yousaf, Saad N and Joshi, Victoria S and Britt, John E and Rose, Chad G and O{\textquoteright}Malley, Marcia K} } @article {o2019expert, title = {Expert Surgeons Can Smoothly Control Robotic Tools With a Discrete Control Interface}, journal = {IEEE Transactions on Human-Machine Systems}, volume = {49}, number = {4}, year = {2019}, pages = {388{\textendash}394}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/THMS2019_O\%27Malley-et-al.pdf}, author = {O{\textquoteright}Malley, Marcia K and Byrne, Michael D and Estrada, Sean and Duran, Cassidy and Schulz, Daryl and Bismuth, Jean} } @article {1940, title = {Improving short-term retention after robotic training by leveraging fixed-gain controllers}, journal = {Journal of Rehabilitation and Assistive Technologies Engineering}, volume = {6}, year = {2019}, month = {01/2019}, abstract = {

IntroductionWhen developing control strategies for robotic rehabilitation, it is important that end-users who train with those strategies retain what they learn. Within the current state-of-the-art, however, it remains unclear what types of robotic controllers are best suited for promoting retention. In this work, we experimentally compare short-term retention in able-bodied end-users after training with two common types of robotic control strategies: fixed- and variable-gain controllers.MethodsOur approach is based on recent motor learning research, where reward signals are employed to reinforce the learning process. We extend this approach to now include robotic controllers, so that participants are trained with a robotic control strategy and auditory reward-based reinforcement on tasks of different difficulty. We then explore retention after the robotic feedback is removed.ResultsOverall, our results indicate that fixed-gain control strategies better stabilize able-bodied users{\textquoteright} motor adaptation than either a no controller baseline or variable-gain strategy. When breaking these results down by task difficulty, we find that assistive and resistive fixed-gain controllers lead to better short-term retention on less challenging tasks but have opposite effects on the learning and forgetting rates.ConclusionsThis suggests that we can improve short-term retention after robotic training with consistent controllers that match the task difficulty.

}, keywords = {Control systems, haptic device, motor learning, neurorehabilitation, Robot-assisted rehabilitation}, doi = {10.1177/2055668319866311}, url = {https://doi.org/10.1177/2055668319866311}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Losey2019RATE.pdf}, author = {Dylan P Losey and Laura H Blumenschein and Janelle P Clark and Marcia K O{\textquoteright}Malley} } @proceedings {1938, title = {On the role of wearable haptics for force feedback in teleimpedance control for dual-arm robotic teleoperation}, year = {2019}, publisher = {IEEE}, abstract = {Robotic teleoperation enables humans to safely complete exploratory procedures in remote locations for applications such as deep sea exploration or building assessments following natural disasters. Successful task completion requires meaningful dual arm robotic coordination and proper understanding of the environment. While these capabilities are inherent to humans via impedance regulation and haptic interactions, they can be challenging to achieve in telerobotic systems. Teleimpedance control has allowed impedance regulation in such applications, and bilateral teleoperation systems aim to restore haptic sensation to the operator, though often at the expense of stability or workspace size. Wearable haptic devices have the potential to apprise the operator of key forces during task completion while maintaining stability and transparency. In this paper, we evaluate the impact of wearable haptics for force feedback in teleimpedance control for dual-arm robotic teleoperation. Participants completed a peg-in-hole, box placement task, aiming to seat as many boxes as possible within the trial period. Experiments were conducted both transparent and opaque boxes. With the opaque box, participants achieved a higher number of successful placements with haptic feedback, and we saw higher mean interaction forces. Results suggest that the provision of wearable haptic feedback may increase confidence when visual cues are obscured.}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Clark_2019_ICRA_TeleimpedanceWithHaptics_0.pdf}, author = {Clark, Janelle P and Lentini, Gianluca and Barontini, Federica and Catalano, Manuel G and Bianchi, Matteo and O{\textquoteright}Malley, Marcia K} } @article {article, title = {Skin stretch haptic feedback to convey closure information in anthropomorphic, under-actuated upper limb soft prostheses}, journal = {IEEE Transactions on Haptics}, volume = {12}, number = {4}, year = {2019}, month = {2019 December}, pages = {508 - 520}, doi = {10.1109/TOH.2019.2915075}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Battaglia_ToH_2019.pdf}, author = {Battaglia, Edoardo and Clark, Janelle and Bianchi, Matteo and Catalano, Manuel and Bicchi, Antonio and O{\textquoteright}Malley, Marcia} } @article {1934, title = {Spatially Separating Haptic Guidance from Task Dynamics through Wearable Devices.}, journal = {IEEE Trans Haptics}, volume = {12}, number = {4}, year = {2019}, month = {2019 December}, pages = {581 - 593}, abstract = {

Haptic devices have a high potential for delivering tailored training to novices. These devices can simulate forces associated with real-world tasks, or provide guidance forces that convey task completion and learning strategies. It has been shown, however, that providing both task forces and guidance forces simultaneously through the same haptic interface can lead to novices depending on guidance, being unable to demonstrate skill transfer, or learning the wrong task altogether. This work presents a novel solution whereby task forces are relayed via a kinesthetic haptic interface, while guidance forces are spatially separated through a cutaneous skin stretch modality. We explore different methods of delivering cutaneous based guidance to subjects in a dynamic trajectory following task. We next compare cutaneous guidance to kinesthetic guidance, as is traditional to spatially separated assistance. We further investigate the role of placing cutaneous guidance ipsilateral versus contralateral to the task force device. The efficacies of each guidance condition are compared by examining subject error and movement smoothness. Results show that cutaneous guidance can be as effective as kinesthetic guidance, making it a practical and cost-effective alternative for spatially separated assistance.

}, issn = {2329-4051}, doi = {10.1109/TOH.2019.2919281}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/pezent_toh2019.pdf}, author = {Pezent, Evan and Fani, Simone and Clark, Janelle and Bianchi, Matteo and OMalley, Marcia K} } @proceedings {1945, title = {Tasbi: Multisensory Squeeze and Vibrotactile Wrist Haptics for Augmented and Virtual Reality}, year = {2019}, month = {July}, keywords = {augmented reality, computing interfaces, fully immerse users, hand interactions, Haptic interfaces, multisensory haptic wristband, multisensory squeeze, pseudohaptic effects, purely normal squeeze forces, sensory substitution device, Skin, Tactile feedback, Tasbi device, vibrotactile feedback, vibrotactile wrist haptics, virtual button, Virtual reality, virtual world, visual information, wearable devices}, doi = {10.1109/WHC.2019.8816098}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/tasbi_whc2019.pdf}, author = {E. Pezent and A. Israr and M. Samad and S. Robinson and P. Agarwal and H. Benko and N. Colonnese} } @article {1859, title = {Closure to {\textquotedblleft}A review of intent detection, arbitration, and communication aspects of shared control for physical human-robot interaction"}, journal = {ASME Applied Mechanics Reviews}, volume = {70}, number = {1}, year = {2018}, month = {02/2018}, abstract = {

In their discussion article on our review paper, Professors James Schmiedeler and Patrick Wensing have provided an insightful and informative perspective of the roles of intent detection, arbitration, and communication as three pillars of a framework for the implementation of shared control in physical human{\textendash}robot interaction (pHRI). The authors both have significant expertise and experience in robotics, bipedal walking, and robotic rehabilitation. Their commentary introduces commonalities between the themes of the review paper and issues in locomotion with the aid of an exoskeleton or lower-limb prostheses, and presents several important topics that warrant further exploration. These include mechanical design as it pertains to the physical coupling between human and robot, modeling the human to improve intent detection and the arbitration of control, and finite-state machines as an approach for implementation. In this closure, we provide additional thoughts and discussion of these topics as they relate to pHRI.

}, doi = {10.1115/1.4039225}, url = {http://appliedmechanicsreviews.asmedigitalcollection.asme.org/article.aspx?articleID=2672398}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/amr_2018_closure.pdf}, author = {Dylan P. Losey and Craig G. McDonald and Edoardo Battaglia and Marcia K. O{\textquoteright}Malley} } @proceedings {1913, title = {Conveying Language Through Haptics: A Multi-sensory Approach}, year = {2018}, month = {10/2018}, publisher = {ACM}, address = {Singapore}, keywords = {haptics, multi-sensory, speech, wearable}, isbn = {978-1-4503-5967-2}, doi = {10.1145/3267242.3267244}, url = {http://doi.acm.org/10.1145/3267242.3267244}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/NathanDunkelberger_ISWC.pdf}, author = {Dunkelberger, Nathan and Sullivan, Jenny and Bradley, Joshua and Walling, Nickolas P and Manickam, Indu and Dasarathy, Gautam and Israr, Ali and Lau, Frances W. Y. and Klumb, Keith and Knott, Brian and Abnousi, Freddy and Baraniuk, Richard and O{\textquoteright}Malley, Marcia K} } @proceedings {1920, title = {Cycloidal Geartrain In-Use Efficiency Study}, year = {2018}, month = {08/2018}, publisher = {American Society of Mechanical Engineers}, address = {Quebec, Canada}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Farrell2018IDETC.pdf}, author = {Farrell, Logan C and Holley, James and Bluethmann, William and O{\textquoteright}Malley, Marcia K} } @article {PMID:28583735, title = {Electromagnetic tracking of flexible robotic catheters enables "assisted navigation" and brings automation to endovascular navigation in an in vitro study}, journal = {Journal of vascular surgery}, volume = {67}, number = {4}, year = {2018}, month = {06/2017}, pages = {1274{\textendash}1281}, issn = {0741-5214}, doi = {10.1016/j.jvs.2017.01.072}, url = {https://doi.org/10.1016/j.jvs.2017.01.072}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Schwein2017JVS.pdf}, author = {Schwein, Adeline and Kramer, Benjamin and Chinnadurai, Ponraj and Virmani, Neha and Walker, Sean and O{\textquoteright}Malley, Marcia and Lumsden, Alan B and Bismuth, Jean} } @proceedings {1902, title = {Improving Perception Accuracy with Multi-sensory Haptic Cue Delivery}, volume = {II}, year = {2018}, month = {June 13-16}, pages = {289-301}, publisher = {Springer International Publishing}, address = {Pisa, Italy}, abstract = {

This paper presents a novel, wearable, and multi-sensory haptic feedback system intended to support the transmission of large sets of haptic cues that are accurately perceived by the human user. Previous devices have focused on the optimization of haptic cue transmission using a single modality and have typically employed arrays of haptic tactile actuators to maximize information throughput to a user. However, when large cue sets are to be transmitted, perceptual interference between transmitted cues can decrease the efficacy of single-sensory systems. Therefore, we present MISSIVE (Multi-sensory Interface of Stretch, Squeeze, and Integrated Vibration Elements), a wearable system that conveys multi-sensory haptic cues to the user{\textquoteright}s upper arm, allowing for increased perceptual accuracy compared to a single-sensory vibrotactile array of a comparable size, conveying the same number of cues. Our multi-sensory haptic cues are comprised of concurrently rendered, yet perceptually distinct elements: radial squeeze, lateral skin stretch, and localized cutaneous vibration. Our experiments demonstrate that our approach can increase perceptual accuracy compared to a single-sensory vibrotactile system of comparable size and that users prefer MISSIVE.

}, isbn = {978-3-319-93399-3}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/dunkelberger\%202018\%20eurohaptics\%20compressed.pdf}, author = {Dunkelberger, Nathan and Bradley, Joshua and Sullivan, Jennifer L. and Israr, Ali and Lau, Frances and Klumb, Keith and Abnousi, Freddy and O{\textquoteright}Malley, Marcia K.}, editor = {Prattichizzo, Domenico and Shinoda, Hiroyuki and Tan, Hong Z. and Ruffaldi, Emanuele and Frisoli, Antonio} } @proceedings {1862, title = {Learning from Physical Human Corrections, One Feature at a Time}, year = {2018}, month = {03/2018}, publisher = {ACM/IEEE}, address = {Chicago, USA}, abstract = {

We focus on learning robot objective functions from human guidance: specifically, from physical corrections provided by the person while the robot is acting. Objective functions are typically parametrized in terms of features, which capture aspects of the task that might be important. When the person intervenes to correct the robot{\textquoteright}s behavior, the robot should update its understanding of which features matter, how much, and in what way. Unfortunately, real users do not provide optimal corrections that isolate exactly what the robot was doing wrong. Thus, when receiving a correction, it is difficult for the robot to determine which features the person meant to correct, and which features were changed unintentionally. In this paper, we propose to improve the efficiency of robot learning during physical interactions by reducing unintended learning. Our approach allows the human-robot team to focus on learning one feature at a time, unlike state-of-the-art techniques that update all features at once. We derive an online method for identifying the single feature which the human is trying to change during physical interaction, and experimentally compare this one-at-a-time approach to the all-at-once baseline in a user study. Our results suggest that users teaching one-at-a-time perform better, especially in tasks that require changing multiple features.

}, doi = {10.1145/3171221.3171267}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Losey_HRI2018.pdf}, author = {Andrea Bajcsy and Dylan P. Losey and Marcia K. O{\textquoteright}Malley and Anca D. Dragan} } @article {1858, title = {A review of intent detection, arbitration, and communication aspects of shared control for physical human-robot interaction}, journal = {ASME Applied Mechanics Reviews}, volume = {70}, number = {1}, year = {2018}, month = {02/2018}, abstract = {

As robotic devices are applied to problems beyond traditional manufacturing and industrial settings, we find that interaction between robots and humans, especially physical interaction, has become a fast developing field. Consider the application of robotics in healthcare, where we find telerobotic devices in the operating room facilitating dexterous surgical procedures, exoskeletons in the rehabilitation domain as walking aids and upper-limb movement assist devices, and even robotic limbs that are physically integrated with amputees who seek to restore their independence and mobility. In each of these scenarios, the physical coupling between human and robot, often termed physical human robot interaction (pHRI), facilitates new human performance capabilities and creates an opportunity to explore the sharing of task execution and control between humans and robots. In this review, we provide a unifying view of human and robot sharing task execution in scenarios where collaboration and cooperation between the two entities are necessary, and where the physical coupling of human and robot is a vital aspect. We define three key themes that emerge in these shared control scenarios, namely, intent detection, arbitration, and feedback. First, we explore methods for how the coupled pHRI system can detect what the human is trying to do, and how the physical coupling itself can be leveraged to detect intent. Second, once the human intent is known, we explore techniques for sharing and modulating control of the coupled system between robot and human operator. Finally, we survey methods for informing the human operator of the state of the coupled system, or the characteristics of the environment with which the pHRI system is interacting. At the conclusion of the survey, we present two case studies that exemplify shared control in pHRI systems, and specifically highlight the approaches used for the three key themes of intent detection, arbitration, and feedback for applications of upper limb robotic rehabilitation and haptic feedback from a robotic prosthesis for the upper limb.

}, doi = {DOI: 10.1115/1.4039145}, url = {http://appliedmechanicsreviews.asmedigitalcollection.asme.org/article.aspx?articleID=2671581}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/amr_2018_review.pdf}, author = {Dylan P. Losey and Craig G. McDonald and Edoardo Battaglia and Marcia K. O{\textquoteright}Malley} } @proceedings {1921, title = {Separating haptic guidance from task dynamics: A practical solution via cutaneous devices}, year = {2018}, month = {03/2018}, publisher = {IEEE}, address = {San Francisco, CA}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/pezent2018hs.pdf}, author = {Pezent, Evan and Fani, Simone and Bradley, Joshua and Bianchi, Matteo and O{\textquoteright}Malley, Marcia K} } @proceedings {1903, title = {Toward improved surgical training: Delivering smoothness feedback using haptic cues}, year = {2018}, month = {03/2018}, pages = {241-246}, publisher = {IEEE}, address = {San Francisco, CA}, abstract = {

Surgery is a challenging domain for motor skill acquisition, and compounding this difficulty is the often delayed and qualitative nature of feedback that is provided to trainees. In this paper, we explore the effectiveness of providing real-time feedback of movement smoothness, a characteristic associated with skilled and coordinated movement, via a vibrotactile cue. Subjects performed a mirror-tracing task that requires coordination and dexterity similar in nature to that required in endovascular surgery. Movement smoothness, measured by spectral arc length, a frequency-domain measure of movement smoothness, was encoded in a vibrotactile cue. Performance of the mirror tracing task with smoothness-based feedback was compared to position-based feedback (where the subject was alerted when they moved outside the path boundary) and to a no-feedback control condition. Although results of this pilot study failed to indicate a statistically significant effect of smoothness-based feedback on performance, subjects receiving smoothness-based feedback altered their task completion strategies to improve speed and accuracy, while those receiving position-based feedback or no feedback only improved in terms of increased accuracy. In tasks such as surgery where both speed and accuracy are vital to positive patient outcomes, the provision of smoothness-based feedback to the surgeon has the potential to positively influence performance.

}, keywords = {biomechanics, biomedical education, computer based training, coordinated movement, delayed nature, dexterity, Feedback, frequency-domain measure, haptic cues, Haptic interfaces, Measurement, medical computing, mirror tracing task, mirror-tracing task, Mirrors, motor skill acquisition, movement smoothness, Navigation, qualitative nature, real-time feedback, skilled movement, smoothness-based feedback, spectral arc length, surgery, surgical training, Task analysis, training, vibrotactile cue}, doi = {10.1109/HAPTICS.2018.8357183}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/jantscher\%202018\%20ieee.pdf}, author = {W. H. Jantscher and S. Pandey and P. Agarwal and S. H. Richardson and B. R. Lin and M. D. Byrne and M. K. O{\textquoteright}Malley} } @proceedings {1828, title = {A Ball and Beam Module for a Haptic Paddle Education Platform}, year = {2017}, month = {10/2017}, publisher = {ASME}, address = {Tysons, VA}, abstract = {
Single degree of freedom force-feedback mechatronic devices,\ often called haptic paddles, are used in university curriculum
as well as massive open online courses (MOOCs). While devices\ differ based on the goals of a given course, broadly speaking
they provide hands-on learning for students studying mechatronics\ and dynamics. We introduce the third iteration of the
Haptic Paddle at Rice University, which has been modified to\ improve haptic performance and robustness. The modifications
to the design increased device up time as well as the devices Z-width.\ The performance improvement enables the addition of
experimental plants to the haptic paddle base, which can be directed\ at advanced dynamics and controls courses, or special
topics in mechatronics and haptics. The first module, a Haptic\ Ball and Beam, adds an underactuated plant for teleoperation or
more complex control structures, and a testbed for haptic motor\ learning experiments in undergraduate coursework.
}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/DSCC_BB_HP_Platform-min.pdf}, author = {Rose, Chad G. and Bucki, Nathan L. and O{\textquoteright}Malley, Marcia K.} } @proceedings {1885, title = {A Cable-based Series Elastic Actuator with Conduit Sensor for Wearable Exoskeletons}, year = {2017}, month = {05/2017}, publisher = {IEEE}, address = {Singapore}, keywords = {actuation system design, Actuators, cable tension control, cable tension measurement, cable-based series elastic actuator, cable-conduit transmission, cables (mechanical), compliance control, compliant force sensor, conduit sensor, DC motor, DC motors, deflection measurement, dynamic effect, Exoskeletons, Feedback, flexible cable conduit transmission, Force, Force control, force sensors, full wearable exosuit, gearbox, Hall effect sensors, Hall effect transducers, human arm, human-robot interaction, Impedance, Magnetic flux, physical assistance, robot dynamics, Robots, series elastic force sensor, soft exosuit, soft wearable exoskeleton, springs (mechanical), translational steel compression spring, transmission conduit, user interface, virtual impedance, wearable robotic device}, doi = {10.1109/ICRA.2017.7989790}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/blumenschein2017ieee.pdf}, author = {L. H. Blumenschein and C. G. McDonald and M. K. O{\textquoteright}Malley} } @proceedings {1923, title = {Combining functional electrical stimulation and a powered exoskeleton to control elbow flexion}, year = {2017}, month = {11/2017}, pages = {87-88}, keywords = {Elbow, elbow flexion, Exoskeletons, extension trajectory, functional electrical stimulation, hybrid FES, hybrid system, Iron, medical robotics, Muscles, neuromuscular stimulation, Patient rehabilitation, robotic exoskeleton system, Robots, Torque, Trajectory, upper-limb paralysis}, doi = {10.1109/WEROB.2017.8383860}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Wolf2017werob.pdf}, author = {D. Wolf and N. Dunkelberger and C. G. McDonald and K. Rudy and C. Beck and M. K. O{\textquoteright}Malley and E. Schearer} } @proceedings {1811, title = {The Effect of Robot Dynamics on Smoothness during Wrist Pointing}, year = {2017}, month = {08/2017}, publisher = {IEEE}, address = {London, England}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Erwin2017\%20-\%20Robot\%20impacts\%20movements.pdf}, author = {Erwin,Andrew and Pezent,Evan and Bradley,Joshua and O{\textquoteright}Malley, M.K.} } @proceedings {1884, title = {On the Efficacy of Isolating Shoulder and Elbow Movements with a Soft, Portable, and Wearable Robotic Device}, volume = {16}, year = {2017}, pages = {89-94}, publisher = {Springer International Publishing}, address = {Springer, Cham}, abstract = {

Treatment intensity has a profound effect on motor recovery following neurological injury. The use of robotics has potential to automate these labor-intensive therapy procedures that are typically performed by physical therapists. Further, the use of wearable robotics offers an aspect of portability that may allow for rehabilitation outside the clinic. The authors have developed a soft, portable, lightweight upper extremity wearable robotic device to provide motor rehabilitation of patients with affected upper limbs due to traumatic brain injury (TBI). A key feature of the device demonstrated in this paper is the isolation of shoulder and elbow movements necessary for effective rehabilitation interventions. Herein is presented a feasibility study with one subject and demonstration of the device{\textquoteright}s ability to provide safe, comfortable, and controlled upper extremity movements. Moreover, it is shown that by decoupling shoulder and elbow motions, desired isolated joint actuation can be achieved.

}, isbn = {978-3-319-46532-6}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/kadivar2016werob.pdf}, author = {Kadivar, Zahra and Beck, Christopher E. and Rovekamp, Roger N. and O{\textquoteright}Malley, Marcia K. and Joyce, Charles A.}, editor = {Gonz{\'a}lez-Vargas, Jos{\'e} and Ib{\'a}{\~n}ez, Jaime and Contreras-Vidal, Jose L. and van der Kooij, Herman and Pons, Jos{\'e} Luis} } @proceedings {1816, title = {Improving robotic stroke rehabilitation by incorporating neural intent detection: Preliminary results from a clinical trial}, year = {2017}, month = {07/2018}, publisher = {IEEE}, address = {London, UK}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Sullivan_ICORR\%202017_BMI\%20Exo.pdf}, author = {Sullivan, J.L. and Bhagat, N.A. and Yozbatiran, N. and Paranjape, R. and Losey, C.G. and Grossman, R.G. and Contreras-Vidal, J.L. and Francisco, G.E. and O{\textquoteright}Malley, M.K.} } @proceedings {1830, title = {Learning Robot Objectives from Physical Human Interaction}, year = {2017}, month = {11/2017}, pages = {217-226}, publisher = {PMLR}, address = {Mountain View, CA}, abstract = {

When humans and robots work in close proximity, physical interaction is inevitable. Traditionally, robots treat physical interaction as a disturbance, and resume their original behavior after the interaction ends. In contrast, we argue that physical human interaction is informative: it is useful information about how the robot should be doing its task. We formalize learning from such interactions as a dynamical system in which the task objective has parameters that are part of the hidden state, and physical human interactions are observations about these parameters. We derive an online approximation of the robot{\textquoteright}s optimal policy in this system, and test it in a user study. The results suggest that learning from physical interaction leads to better robot task performance with less human effort.

}, keywords = {learning from demonstration, physical human-robot interaction}, url = {http://proceedings.mlr.press/v78/bajcsy17a.html}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/CoRL_2017.pdf}, author = {Andrea Bajcsy and Dylan P. Losey and Marcia K. O{\textquoteright}Malley and Anca D. Dragan} } @proceedings {1795, title = {The Rice Haptic Rocker: skin stretch haptic feedback with the Pisa/IIT SoftHand}, year = {2017}, month = {06/2017}, publisher = {IEEE}, address = {Munich, Germany}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/CameraReady.pdf}, author = {Edoardo Battaglia and Janelle P. Clark and Matteo Bianchi and Manuel G. Catalano and Antonio Bicchi and Marcia K. O{\textquoteright}Malley} } @article {PMID:28857769, title = {Robot-Assisted Training of Arm and Hand Movement Shows Functional Improvements for Incomplete Cervical Spinal Cord Injury}, journal = {American Journal of Physical Medicine \& Rehabilitation}, volume = {96}, number = {10}, year = {2017}, month = {10/2017}, pages = {S171{\textemdash}S177}, issn = {0894-9115}, doi = {10.1097/phm.0000000000000815}, url = {https://doi.org/10.1097/PHM.0000000000000815}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/francisco2017AJPRM.pdf}, author = {Francisco, Gerard E and Yozbatiran, Nuray and Berliner, Jeffrey and O'Malley, Marcia K and Pehlivan, Ali Utku and Kadivar, Zahra and Fitle, Kyle and Boake, Corwin} } @proceedings {1892, title = {Simply Grasping Simple Shapes: Commanding a Humanoid Hand with a Shape-Based Synergy}, year = {2017}, month = {12/2017}, address = {Puerto Varas, Chile}, abstract = {

Despite rapid advancements in dexterity and mechanical design, the utility of humanoid robots outside of a controlled laboratory setting is limited in part due to the complexity involved in programming robots to grasp common objects. There exists a need for an efficient method to command high degree-of-freedom (DoF) position-controlled dexterous manipulators to grasp a range of objects such that explicit models are not needed for every interaction. The authors propose a method termed geometrical synergies that, similar to the neuroscience concept of postural synergies, aims to decrease the commanded DoF of the humanoid hand. In the geometrical synergy approach, the method relies on grasp design based on intuitive measurements of the object to be grasped, in contrast to postural synergy methods that focus on the principal components of human grasps to determine robot hand joint commands. For this paper, a synergy was designed to grasp cylinder-shaped objects. Using the SynGrasp toolbox, a model of a twelve-DoF hand was created to perform contact analysis around a small set of cylinders dened by a single variable, diameter. Experiments were performed with the robot to validate and update the synergy-based models. Successful manipulation of a large range of cylindrical objects not previously introduced to the robot was demonstrated. This geometric synergy-based grasp planning method can be applied to any position-controlled humanoid hand to decrease the number of commanded DoF based on simple, measureable inputs in order to grasp commonly shaped objects. This method has the potential to vastly expand the library of objects the robot can manipulate.

}, keywords = {Dexterous Hand, Grasp, Humanoid, Manipulation, Synergy}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/farrell2017simply.pdf}, author = {Logan C. Farrell and Troy A. Dennis and Julia A. Badger and Marcia K. O{\textquoteright}Malley} } @proceedings {1890, title = {Toward training surgeons with motion-based feedback: Initial validation of smoothness as a measure of motor learning}, volume = {61}, number = {1}, year = {2017}, pages = {1531-1535}, abstract = {

Surgery is a challenging domain for motor skill acquisition. A critical contributing factor in this difficulty is that feedback is often delayed from performance and qualitative in nature. Collection of highdensity motion information may offer a solution. Metrics derived from this motion capture, in particular indices of movement smoothness, have been shown to correlate with task outcomes in multiple domains, including endovascular surgery. The open question is whether providing feedback based on these metrics can be used to accelerate learning. In pursuit of that goal, we examined the relationship between a motion metric that is computationally simple to compute{\textemdash}spectral arc length{\textemdash}and performance on a simple but challenging motor task, mirror tracing. We were able to replicate previous results showing that movement smoothness measures are linked to overall performance, and now have performance thresholds to use in subsequent work on using these metrics for training.

}, doi = {10.1177/1541931213601747}, url = {https://doi.org/10.1177/1541931213601747}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/pandey2017hfes.pdf}, author = {Shivam Pandey and Michael D. Byrne and William H. Jantscher and Marcia K. O{\textquoteright}Malley and Priyanshu Agarwal} } @article {1777, title = {Design and optimization of an EEG-based brain machine interface (BMI) to an upper-limb exoskeleton for stroke survivors}, journal = {Frontiers in Neuroscience}, volume = {10}, year = {2016}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/FINS2016.pdf}, author = {Bhagat, N.A. and Venkatakrishnan, A. and Abibullaev, B. and Artz, E.J. and Yozbatiran, N. and Blank, A.A. and French, J. and Karmonik, C. and Grossman, R.G. and O{\textquoteright}Malley, M.K. and Francisco, G. and Contreras-Vidal, J.L.} } @article {1759, title = {Flexible robotics with electromagnetic tracking improve safety and efficiency during in vitro endovascular navigation}, journal = {Journal of Vascular Surgery}, volume = {63}, number = {1}, year = {2016}, pages = {285-286}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Schwein2016\%20-\%20Flexible\%20robotics\%20tracking.pdf}, author = {Adeline Schwein and Kramer, B.D. and Ponraj Chinnadurai and Sean Walker and O{\textquoteright}Malley, M.K. and Alan Lumsden and Jean Bismuth} } @proceedings {1822, title = {Improving the retention of motor skills after reward-based reinforcement by incorporating haptic guidance and error augmentation}, year = {2016}, pages = {857-863}, abstract = {

There has been significant research aimed at leveraging programmable robotic devices to provide haptic assistance or augmentation to a human user so that new motor skills can be trained efficiently and retained long after training has concluded. The success of these approaches has been varied, and retention of skill is typically not significantly better for groups exposed to these controllers during training. These findings point to a need to incorporate a more complete understanding of human motor learning principles when designing haptic interactions with the trainee. Reward-based reinforcement has been studied for its role in improving retention of skills. Haptic guidance, which assists a user to complete a task, and error augmentation, which exaggerates error in order to enhance feedback to the user, have been shown to be beneficial for training depending on the task difficulty, subject ability, and task type. In this paper, we combine the presentation of reward-based reinforcement with these robotic controllers to evaluate their impact on retention of motor skill in a visual rotation task with tunable difficulty using either fixed or moving targets. We found that with the reward-based feedback paradigm, both haptic guidance and error augmentation led to better retention of the desired visuomotor offset during a simple task, while during a more complex task, only subjects trained with haptic guidance demonstrated performance superior to those trained without a controller.

}, isbn = {978-1-5090-3287-7}, issn = {978-1-5090-3287-7}, doi = {10.1109/BIOROB.2016.7523735}, url = {http://ieeexplore.ieee.org/abstract/document/7523735/}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Losey_BioRob_Improving.pdf}, author = {Dylan P. Losey and Laura H. Blumenschein and Marcia K. O{\textquoteright}Malley} } @article {1768, title = {Smoothness of surgical tool tip motion correlates to skill in endovascular tasks}, journal = {IEEE Transactions on Human Machine Systems}, volume = {46}, number = {5}, year = {2016}, pages = {647-659}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/J23Estrada_press.pdf}, author = {Estrada, S. and Duran, C. and Schulz, D. and Bismuth, J. and Byrne, M.D. and O{\textquoteright}Malley, M.K.} } @proceedings {1765, title = {Acumen: An open-source testbed for cyber-physical systems research}, year = {2015}, month = {10/2015}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/cyclone15Taha.pdf}, author = {Walid Taha and Adam Duracz and Yingfu Zeng and Kevin Atkinson and Ferenc A.Bartha and Paul Brauner and Jan Duracz and Fei Xu and Robert Cartwright and Michal Konecny and Eugenio Moggi and Jawad Masood and Pererik Andreasson and Jun Inoue and Anita Santanna and Roland Philippsen and Alexandre Chapoutot and O{\textquoteright}Malley, M.K. and Aaron Ames and Veronica Gaspes and Lise Hvatum and Shyam Mehta and Henrik Eriksson and Christian Grante} } @article {1761, title = {An exploration of grip force regulation with a low-impedance myoelectric prosthesis featuring referred haptic feedback}, journal = {Journal of Neuroengineering and Rehabilitation}, volume = {12}, year = {2015}, doi = {10.1186/s12984-015-0098-1}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Brown2015\%20-\%20Grip\%20force\%20regulation.pdf}, author = {J.D. Brown and A. Paek and M. Syed and O{\textquoteright}Malley, M.K. and P.A. Shewokis and J.L. Contreras-Vidal and R.B. Gillespie and A.J. Davis} } @article {1729, title = {Kinematics effectively delineate accomplished users of endovascular robotics with a physical training model}, journal = {Journal of Vascular Surgery}, volume = {61}, number = {2}, year = {2015}, pages = {535-541}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Duran_et_al_JVS2015.pdf}, author = {Cassidy Duran and Sean Estrada and Marcia O{\textquoteright}Malley and Alan B. Lumsden and Jean Bismuth} } @article {1762, title = {The model for Fundamentals of Endovascular Surgery (FEVS) successfully defines the competent endovascular surgeon}, journal = {Journal of Vascular Surgery}, volume = {62}, number = {6}, year = {2015}, pages = {1660-1666}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/2015_JVS_Duran_press.pdf}, author = {Cassidy Duran and Sean Estrada and O{\textquoteright}Malley, M.K. and Malachi Sheahan and Murray Shames and Jason T Lee and Jean Bismuth} } @proceedings {1754, title = {Proportional sEMG based robotic assistance in an isolated wrist movement}, year = {2015}, month = {10/2015}, address = {Columbus, Ohio}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Atz2015\%20-\%20sEMG\%20based\%20robotic\%20assistance.pdf}, author = {Artz, E.J. and Blank, Amy A. and O{\textquoteright}Malley, M.K.} } @proceedings {1767, title = {The role of auxiliary and referred haptic feedback in myoelectric control}, year = {2015}, month = {06/2015}, pages = {13-18}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Treadway2015\%20-\%20Haptic\%20feedback\%20myoelectric\%20control.pdf}, author = {Treadway, Emma and Gillespie, B and Bolger, D. and Blank, A. and O{\textquoteright}Malley, M.K. and Davis, A.} } @article {7080902, title = {Tactile Feedback of Object Slip Facilitates Virtual Object Manipulation}, journal = {Haptics, IEEE Transactions on}, volume = {PP}, number = {99}, year = {2015}, pages = {1-1}, keywords = {Force, force feedback, haptics, Phantoms, prosthetics, slip feedback, tactile sensors, vibrotactile feedback, Visualization}, issn = {1939-1412}, doi = {10.1109/TOH.2015.2420096}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/WalkerToH2015small.pdf}, author = {Walker, J. and Blank, A. and Shewokis, P. and O{\textquoteright}Malley, M.} } @article {1719, title = {Current Trends in Robot-Assisted Upper-Limb Stroke Rehabilitation: Promoting Patient Engagement in Therapy}, journal = {Current Physical Medicine and Rehabilitation Reports}, year = {2014}, doi = {10.1007/s40141-014-0056-z}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/2014_CPMRR_press.pdf}, author = {Amy A Blank and James A French and Ali Utku Pehlivan and Marcia K O{\textquoteright}Malley} } @proceedings {1882, title = {Detecting movement intent from scalp EEG in a novel upper limb robotic rehabilitation system for stroke}, year = {2014}, month = {08/2014}, keywords = {Accuracy, Adult, bioelectric potentials, brain-computer interfaces, closed loop systems, closed-loop brain-machine interfaces, Computer-Assisted, diseases, electroencephalography, Electromyography, Exoskeletons, hemiparesis, Humans, Male, medical robotics, medical signal detection, medical signal processing, Middle Aged, Movement, movement intent detection, neurophysiology, Paresis, Patient rehabilitation, Robotics, Robots, scalp electroencephalography, Signal Processing, stroke, stroke rehabilitation, Support Vector Machine, Support vector machines, training, Upper Extremity, upper extremity dysfunction, upper limb robotic rehabilitation system, Young Adult}, doi = {10.1109/EMBC.2014.6944532}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/bhagat2014ieee.pdf}, author = {N. A. Bhagat and J. French and A. Venkatakrishnan and N. Yozbatiran and G. E. Francisco and M. K. O{\textquoteright}Malley and J. L. Contreras-Vidal} } @proceedings {1883, title = {On the development of objective metrics for surgical skills evaluation based on tool motion}, year = {2014}, publisher = {IEEE}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/estrada2014ieee.pdf}, author = {Estrada, Sean and O{\textquoteright}Malley, Marcia K and Duran, Cassidy and Schulz, Daryl and Bismuth, Jean} } @article {1695, title = {Identifying Successful Motor Task Completion via Motion-Based Performance Metrics}, journal = {Human-Machine Systems, IEEE Transactions on}, volume = {44}, year = {2014}, pages = {139-145}, keywords = {Accelerometers, human{\textendash}computer interaction, motion analysis}, doi = {10.1109/THMS.2013.2290129}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/O\%27Malley_et_al_IEEE_Trans_HMS_PRESS.pdf}, author = {O{\textquoteright}Malley, M.K. and Purkayastha, S.N. and Howie, N. and Byrne, M.D.} } @inbook {1701, title = {Robotics as a Tool for Training and Assessment of Surgical Skill}, booktitle = {Computational Surgery and Dual Training}, year = {2014}, pages = {365-375}, publisher = {Springer New York}, organization = {Springer New York}, keywords = {Assessment, Human{\^a}{\texteuro}{\textquotedblleft}robot interaction, Manual, Performance measures, Rehabilitation robotics, Robotics, Simulators, Skill, Skill training, Surgical, Tasks, Virtual reality}, isbn = {978-1-4614-8647-3}, doi = {10.1007/978-1-4614-8648-0_24}, url = {http://dx.doi.org/10.1007/978-1-4614-8648-0_24}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/CRISP_O\%27Malley_et_al_120611updated.pdf}, author = {O{\textquoteright}Malley, Marcia K. and Celik, Ozkan and Huegel, Joel C. and Byrne, Michael D. and Bismuth, Jean and Dunkin, Brian J. and Goh, Alvin C. and Miles, Brian J.}, editor = {Garbey, Marc and Bass, Barbara Lee and Berceli, Scott and Collet, Christophe and Cerveri, Pietro} } @proceedings {1711, title = {Tactile feedback of object slip improves performance in a grasp and hold task}, year = {2014}, month = {Feb}, doi = {10.1109/HAPTICS.2014.6775499}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/HS2014_SensoryFeedback_Walker_Press.pdf}, author = {Walker, Julie M. and Blank, Amy A. and Shewokis, Patricia A. and O{\textquoteright}Malley, Marcia K.} } @article {1864, title = {Upper Extremity Exoskeletons for Robot Aided Rehabilitation}, journal = {Mechanical Engineering}, volume = {136}, number = {9}, year = {2014}, month = {09}, pages = {S6-S11}, abstract = {

Neurological injuries, including stroke and spinal cord injury, typically result in significant motor impairments. These impairments negatively impact an individual{\textquoteright}s movement coordination, in turn affecting their ability to function independently. Intensively repetitous motion training has proven to restore some motor function after neurological injuries. This training is often labor-intensive and costly. By enabling therapists to train their patients intensively through consistent, repeatable movements, robotic rehabilitation systems offer a cost-effective solution requiring less labor and effort. The design of upper limb robotic therapy devices has been a topic of research for over two decades. Early devices were end-effector based, and guided the motion of a patient{\textquoteright}s hand to desired positions. Hardware and software designs emphasized the safety of the robotic devices, using control methods specifically designed to ensure safe interaction forces between the user and the device.

}, keywords = {5400:Research \& development, 9190:United States, Cost reduction, Engineering{\textendash}Mechanical Engineering, Medical research, Neurological disorders, Robotics, United States{\textendash}US}, isbn = {00256501}, url = {https://search.proquest.com/docview/1559578916?accountid=7064}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/sergi2014asmesmall.pdf}, author = {Sergi,Fabrizio and Blank,Amy and O{\textquoteright}Malley,Marcia} } @inbook {1700, title = {Workload and Performance Analyses with Haptic and Visually Guided Training in a Dynamic Motor Skill Task}, booktitle = {Computational Surgery and Dual Training}, year = {2014}, pages = {377-387}, publisher = {Springer New York}, organization = {Springer New York}, keywords = {force feedback, Haptics guidance, Joystick, Motor skill, performance, Skill acquisition, training, virtual environment, Workload}, isbn = {978-1-4614-8647-3}, doi = {10.1007/978-1-4614-8648-0_25}, url = {http://dx.doi.org/10.1007/978-1-4614-8648-0_25}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Huegel-NFVWTLX-CRISP.pdf}, author = {Huegel, Joel C. and O{\textquoteright}Malley, Marcia K.}, editor = {Garbey, Marc and Bass, Barbara Lee and Berceli, Scott and Collet, Christophe and Cerveri, Pietro} } @article {1696, title = {Human-Scale Motion Capture with an Accelerometer-Based Gaming Controller}, journal = {Journal of Robotics and Mechatronics}, volume = {25}, number = {3}, year = {2013}, month = {03/2013}, pages = {458-465}, chapter = {458}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/JRM_PRESS_Purkayastha-et-al_2013.pdf}, author = {Purkayastha, Sagar N and Byrne, Michael D and O{\textquoteright}Malley, M.K.} } @proceedings {1704, title = {Modeling Basic Aspects of Cyber-Physical Systems, Part II}, year = {2013}, address = {Tokyo, Japan}, abstract = {
We continue to consider the question of what
language features are needed to effectively model cyber-physical
systems (CPS). In previous work, we proposed using a core
language as a way to study this question, and showed how
several basic aspects of CPS can be modeled clearly in a
language with a small set of constructs. This paper reports
on the result of our analysis of two, more complex, case studies
from the domain of rigid body dynamics. The first one, a
quadcopter, illustrates that previously proposed core language
can support larger, more interesting systems than previously
shown. The second one, a serial robot, provides a concrete
example of why we should add language support for static
partial derivatives, namely that it would significantly improve
the way models of rigid body dynamics can be expressed.
}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/paper\%20\%285\%29.pdf}, author = {Yingfu Zeng and Rose, Chad G. and Paul Branner and Walid Taha and Jawad Masood and Roland Philippsen and Marcia K. O{\textquoteright}Malley and Robert Cartwright} } @proceedings {1691, title = {A Pre-Clinical Framework for Neural Control of a Therapeutic Upper-Limb Exoskeleton}, year = {2013}, month = {2013}, pages = {1159-1162}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/BMI-EXO_2013_NER.pdf}, author = {Amy Blank and Marcia K. O{\textquoteright}Malley and Gerard E. Francisco and Jose L. Contreras-Vidal} } @proceedings {1684, title = {Reconstructing Surface EMG from Scalp EEG during Myoelectric Control of a Closed Looped Prosthetic Device}, year = {2013}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Paek\%20EMBC\%202013.pdf}, author = {Andrew Y. Paek and Jeremy D. Brown and R. B. Gillespie and Marcia K. O{\textquoteright}Malley and Patricia A. Shewokis and Jose L. Contreras-Vidal} } @proceedings {1685, title = {Understanding the Role of Haptic Feedback in a Teleoperated Grasp and Lift Task}, year = {2013}, month = {04/2013}, pages = {271-276}, abstract = {

Achieving dexterous volitional control of an upper-limb prosthetic device will require multimodal sensory feedback that goes beyond vision. Haptic display is well-positioned to provide this additional sensory information. Haptic display, however, includes a diverse set of modalities that encode information differently. We have begun to make a comparison between two of these modalities, force feedback spanning the elbow, and amplitude-modulated vibrotactile feedback, based on performance in a functional grasp and lift task. In randomly ordered trials, we assessed the performance of N=11 participants (8 able-bodied, 3 amputee) attempting to grasp and lift an object using an EMG controlled gripper under three feedback conditions (no feedback, vibrotactile feedback, and force feed-back), and two object weights that were undetectable by vision. Preliminary results indicate differences between able-bodied and amputee participants in coordination of grasp and lift forces. In addition, both force feedback and vibrotactile feedback contribute to significantly better task performance (fewer slips) and better adaptation following an unpredicted weight change. This suggests that the development and utilization of internal models for predictive control is more intuitive in the presence of haptic feedback.

}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/WH2013_FINAL_PRESS_Brown_et_al.pdf}, author = {Jeremy D. Brown and Andrew Paek and Mashaal Syed and Marcia K. O{\textquoteright}Malley and Patricia Shewokis and Jose L. Contreras-Vidal and R. B. Gillespie} } @proceedings {1471, title = {On the Correlation between Motion Data Captured from Low-Cost Gaming Controller and High Precision Encoders}, year = {2012}, month = {8/2012}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/EMBS2012_FINAL_Purkayastha.pdf}, author = {S.N. Purkayastha and M.D. Byrne and O{\textquoteright}Malley, M.K.} } @article {1480, title = {Outcomes of Recent Efforts at Rice University to Incorporate Entrepreneurship Concepts into Interdisciplinary Capstone Design}, journal = {International Journal of Engineering Education}, volume = {28}, year = {2012}, pages = {1-5}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/oden2012ijee.pdf}, author = {Z.M. Oden and O{\textquoteright}Malley, M.K. and G. Woods and T. Kraft and B. Burke} } @proceedings {1881, title = {Preliminary Results in Virtual Testing for Smart Buildings}, year = {2012}, publisher = {Springer Berlin Heidelberg}, address = {Berlin, Heidelberg}, abstract = {

Smart buildings promise to revolutionize the way we live. Applications ranging from climate control to fire management can have significant impact on the quality and cost of these services. However, a smart building and any technology with direct effect on the safety of its occupants must undergo extensive testing. Virtual testing by means of computer simulation can significantly reduce the cost of testing and, as a result, accelerate the development of novel applications. Unfortunately, building physically-accurate simulation codes can be labor intensive.

}, isbn = {978-3-642-29154-8}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/bruneau2012lnicst.pdf}, author = {Bruneau, Julien and Consel, Charles and O{\textquoteright}Malley, Marcia and Taha, Walid and Hannourah, Wail Masry}, editor = {S{\'e}nac, Patrick and Ott, Max and Seneviratne, Aruna} } @article {1698, title = {Robotic training and clinical assessment of upper extremity movements after spinal cord injury; a single case report}, journal = {Journal of Rehabilitation Medicine}, volume = {44}, year = {2012}, month = {01/2012}, pages = {186-188}, chapter = {186}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/J_Rehab_Medicine_2012_Final_press_version.pdf}, author = {Yozbatiran, Nuray and Berliner, J. and O{\textquoteright}Malley, M.K. and Pehlivan, A.U. and Z. Kadivar and Boake, Corwin and Gerard E. Francisco} } @proceedings {1475, title = {Motor Skill Acquisition in a Virtual Gaming Environment}, year = {2011}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/howie2011hfes.pdf}, author = {N. Howie and S.N. Purkayastha and M.D. Byrne and O{\textquoteright}Malley, M.K.} } @proceedings {1880, title = {Robotic training and clinical assessment of forearm and wrist movements after incomplete spinal cord injury: A case study}, year = {2011}, month = {June}, pages = {619-622}, abstract = {

The effectiveness of a robotic training device was evaluated in a 24-year-old male, cervical level four, ASIA Impairment Scale D injury. Robotic training of both upper extremities was provided for three hr/day for ten consecutive sessions using the RiceWrist, an electrically-actuated forearm and wrist haptic exoskeleton device that has been designed for rehabilitation applications. Training involved wrist flexion/extension, radial/ulnar deviation and forearm supination/pronation. Therapy sessions were tailored, based on the patient{\textquoteright}s movement capabilities for the wrist and forearm, progressed gradually by increasing number of repetitions and resistance. Outcome measures included the ASIA upper-extremity motor score, grip and pinch strength, the Jebsen-Taylor Hand Function test and the Functional Independence Measure. After the training, improvements were observed in pinch strength, and functional tasks. The data from one subject provides valuable information on the feasibility and effectiveness of robotic-assisted training of forearm and hand functions after incomplete spinal cord injury.

}, keywords = {age 24 yr, arm motor function recovery, ASIA upper-extremity motor score, biomechanics, clinical assessment, electrically-actuated forearm, Forearm, forearm movement, forearm pronation, forearm supination, functional independence measure, functional tasks, grip, Haptic interfaces, Humans, injuries, Jebsen-Taylor hand function test, Joints, Male, medical robotics, Medical treatment, Muscles, neurophysiology, patient movement capabilities, Patient rehabilitation, Patient treatment, pinch strength, radial-ulnar deviation, rehabilitation applications, robotic training, Robots, Spinal Cord Injuries, spinal cord injury, training, Wrist, wrist extension, wrist flexion, wrist haptic exoskeleton device, wrist movement, Young Adult}, doi = {10.1109/ICORR.2011.5975425}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/yozbatiran2011ieee.pdf}, author = {N. Yozbatiran and J. Berliner and C. Boake and M. K. O{\textquoteright}Malley and Z. Kadivar and G. E. Francisco} } @inbook {1868, title = {Surgical Robotics: Innovations, Development, and Shortcomings}, booktitle = {Pumps and Pipes}, year = {2011}, pages = {33-44}, publisher = {Springer US}, organization = {Springer US}, address = {Boston, MA}, abstract = {

Robotic devices have been used in the industrial field for over 40 years, while their introduction has been slower into the medical field with many requirements driven by the nature of human tissue and safety. These surgical assistance systems provide intelligent, versatile tools that augment a physician{\textquoteright}s ability to treat patients. Steerable robotic catheters may overcome many of the limitations of standard catheter technology, enhance target vessel cannulation, and reduce instrumentation, while improving overall physician performance. External robotics allows access to a body cavity through percutaneous ports with a high precision, high magnification manipulation of tissue. Robotics-driven imaging systems enhance dynamic data acquisition and provide high speed integration, facilitating image-guided navigation and augmenting other robotic systems. A lack of haptics remains a significant safety issue.

}, isbn = {978-1-4419-6012-2}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Bismuth2011pumpspipes.pdf}, author = {Bismuth, Jean and O{\textquoteright}Malley, Marcia K.}, editor = {Davies, Mark G. and Lumsden, Alan B. and Kline, William E. and Kakadiaris, Ioannis} } @proceedings {899, title = {Analysis and comparison of low cost gaming controllers for motion analysis}, year = {2010}, month = {07/2010}, address = {Montreal, Canada}, abstract = {

Gaming controllers are attractive devices for research due to their onboard sensing capabilities and low cost. However, a proper quantitative analysis regarding their suitability for motion capture has yet to be conducted. In this paper, a detailed analysis of the sensors of two of these controllers, the Nintendo Wiimote and the Sony Playstation 3 Sixaxis is presented. The acceleration data from the sensors were plotted and compared with computed acceleration data derived from a high resolution encoder, then correlated to determine the performance of the gaming controllers. The results show high correlation between the acceleration data of the sensors and the computed acceleration, and more consistency in the sensors of the Sixaxis. The applications of the findings are discussed with respect to potential research ventures.

}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/899-AIM\%20final\%20conference\%20version.pdf}, author = {Purkayastha, Sagar N and Eckenstein, Nick and Byrne, Michael D and O{\textquoteright}Malley, M.K.} } @article {917, title = {Incorporating simulation in vascular surgery education}, journal = {Journal of vascular surgery : official publication, the Society for Vascular Surgery [and] International Society for Cardiovascular Surgery, North American Chapter}, year = {2010}, month = {2010/08/05}, pages = { - }, publisher = {Mosby-Year Book}, isbn = {0741-5214}, url = {http://linkinghub.elsevier.com/retrieve/pii/S0741521410013054?showall=true}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/bismuth2010jvascsurg.pdf}, author = {Jean Bismuth and Michael A. Donovan and O{\textquoteright}Malley, M.K. and Hosam F. El Sayed and Joseph J. Naoum and Eric K. Peden and Mark G. Davies and Alan B. Lumsden} } @article {911, title = {Normalized movement quality measures for therapeutic robots strongly correlate with clinical motor impairment measures}, journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering}, volume = {18}, number = {4}, year = {2010}, pages = {433-444}, abstract = {In this paper, we analyze the correlations between four clinical measures (Fugl{\textendash}Meyer upper extremity scale, Motor Activity Log, Action Research Arm Test, and Jebsen-Taylor Hand Function Test) and four robotic measures (smoothness of movement, trajectory error, average number of target hits per minute, and mean tangential speed), used to assess motor recovery. Data were gathered as part of a hybrid robotic and traditional upper extremity rehabilitation program for nine stroke patients. Smoothness of movement and trajectory error, temporally and spatially normalized measures of movement quality defined for point-to-point movements, were found to have significant moderate to strong correlations with all four of the clinical measures. The strong correlations suggest that smoothness of movement and trajectory error may be used to compare outcomes of different rehabilitation protocols and devices effectively, provide improved resolution for tracking patient progress compared to only pre- and post-treatment measurements, enable accurate adaptation of therapy based on patient progress, and deliver immediate and useful feedback to the patient and therapist.}, url = {http://dx.doi.org/10.1109/TNSRE.2010.2047600}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/911-Celik2010TNSRE.pdf}, author = {Ozkan Celik and O{\textquoteright}Malley, M.K. and Boake, Corwin and H.S. Levin and Yozbatiran, Nuray and Reistetter, Timothy} } @proceedings {1877, title = {A Preliminary ACT-R model of a continuous motor task}, year = {2010}, publisher = {SAGE Publications Sage CA: Los Angeles, CA}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/byme2010hfes.pdf}, author = {Byrne, Michael D and O{\textquoteright}Malley, Marcia K and Gallagher, Melissa A and Purkayastha, Sagar N and Howie, Nicole and Huegel, Joel C} } @proceedings {1876, title = {Toward improved sensorimotor integration and learning using upper-limb prosthetic devices}, year = {2010}, pages = {5077-5080}, publisher = {IEEE}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Gillespie2010ieee.pdf}, author = {Gillespie, R Brent and Contreras-Vidal, Jose Luis and Shewokis, Patricia A and O{\textquoteright}Malley, Marcia K and Brown, Jeremy D and Agashe, Harshavardhan and Gentili, Rodolphe and Davis, Alicia} } @proceedings {1873, title = {Functionally biarticular control for smart prosthetics}, year = {2009}, pages = {627-628}, publisher = {IEEE}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/gillespie2009IEEE.pdf}, author = {Gillespie, Brent and Baker, John and O{\textquoteright}Malley, Marcia and Shewokis, Patricia and Contreras-Vidal, Jose Luis} } @proceedings {celik_comparison, title = {Comparison of robotic and clinical motor function improvement measures for sub-acute stroke patients}, year = {2008}, pages = {2477{\textendash}2482}, address = {Pasadena, CA}, abstract = {

In this paper, preliminary results in motor function improvement for four sub-acute stroke patients that underwent a hybrid robotic and traditional rehabilitation program are presented. The therapy program was scheduled for three days a week, four hours per day (approximately 60\% traditional constraint induced therapy activities and 40\% robotic therapy). A haptic joystick was used to implement four different operating modes for robotic therapy: unassisted (U), constrained (C), assisted (A), and resisted (R) modes. A target hitting task involving the positioning of a pointer on twelve targets was completed by the patients. Two different robotic measures were utilized to quantify the motor function improvement through the sessions: trajectory error (TE) and smoothness of movement (SM). Fugl-Meyer (FM) and motor activity log (MAL) scales were used as clinical measures. Analysis of results showed that the group demonstrates a significant motor function improvement with respect to both clinical and robotic measures. Regression analyses were carried out on corresponding clinical and robotic measure result pairs. A significant relation between FM scale and robotic measures was found for both of the analyzed modes. Regression of robotic measures on MAL scores resulted in no significance. A regression analysis that compared the two clinical measures revealed a very low agreement. Our findings suggest that it might be possible to obtain objective robotic measures that are significantly correlated to widely-used and reliable clinical measures in considerably different operating modes and control schemes.

}, keywords = {robotic rehabilitation}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/74-CelikICRA2008.pdf}, author = {Ozkan Celik and O{\textquoteright}Malley, M.K. and Boake, Corwin and H.S. Levin and Fischer, Steven and Reistetter, Timothy} } @article {080611080561, title = {Design, control and performance of RiceWrist: A force feedback wrist exoskeleton for rehabilitation and training}, journal = {International Journal of Robotics Research}, volume = {27}, number = {2}, year = {2008}, note = {

Feedback wrist exoskeleton;Neurological injuries;

}, pages = {233 - 251}, abstract = {

This paper presents the design, control and performance of a high fidelity four degree-of-freedom wrist exoskeleton robot, RiceWrist, for training and rehabilitation. The RiceWrist is intended to provide kinesthetic feedback during the training of motor skills or rehabilitation of reaching movements. Motivation for such applications is based on findings that show robot-assisted physical therapy aids in the rehabilitation process following neurological injuries. The exoskeleton device accommodates forearm supination and pronation, wrist flexion and extension and radial and ulnar deviation in a compact parallel mechanism design with low friction, zero backlash and high stiffness. As compared to other exoskeleton devices, the RiceWrist allows easy measurement of human joint angles and independent kinesthetic feedback to individual human joints. In this paper, joint-space as well as task-space position controllers and an impedance-based force controller for the device are presented. The kinematic performance of the device is characterized in terms of its workspace, singularities, manipulability, backlash and backdrivability. The dynamic performance of RiceWrist is characterized in terms of motor torque output, joint friction, step responses, behavior under closed loop set-point and trajectory tracking control and display of virtual walls. The device is singularity-free, encompasses most of the natural workspace of the human joints and exhibits low friction, zero-backlash and high manipulability, which are kinematic properties that characterize a high-quality impedance display device. In addition, the device displays fast, accurate response under position control that matches human actuation bandwidth and the capability to display sufficiently hard contact with little coupling between controlled degrees-of-freedom.

}, keywords = {Control systems, Degrees of freedom (mechanics), Feedback, Neurology, Physical therapy, Systems analysis}, url = {http://dx.doi.org/10.1177/0278364907084261}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/48-IJRR-Feb-2008-small.pdf}, author = {Abhishek Gupta and O{\textquoteright}Malley, M.K. and Volkan Patoglu and Burgar, Charles} } @proceedings {064610244127, title = {Adaptation of Haptic Interfaces for a LabVIEW-based System Dynamics Course}, year = {2006}, note = {

Electromechanical systems;LabVIEW graphical programming languages;

}, pages = {147 - 152}, address = {Alexandria, VA, United States}, abstract = {

This paper describes the development of haptic paddle laboratory kits and associated National Instruments LabVIEW virtual instrumentation to support the adaptation of laboratory experiments for a required undergraduate system dynamics course at Rice University. The laboratory experiments use simple haptic interfaces, devices that allow the students to interact via the sense of touch with virtual environments. A clear benefit of this laboratory series is that students study the haptic paddle as a real electromechanical system in addition to using the haptic paddle as a tool to interact with virtual mechanical systems. The haptic paddle hardware has been modified to improve robustness, and the LabVIEW graphical programming language is used for data acquisition and control throughout the laboratory series. {\textcopyright} 2006 IEEE.

}, keywords = {Computer hardware, Curricula, Dynamic programming, Interactive computer systems, Virtual reality}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/35-getPDF.pdf}, author = {Kevin Bowen and O{\textquoteright}Malley, M.K.} } @proceedings {075210997323, title = {Haptic Interfaces for a LabVIEW-based System Dynamics Course}, year = {2006}, note = {

Labview;Course content;Laboratory exercises;Haptic paddles;

}, address = {Chicago, IL, United States}, abstract = {

Too often in undergraduate mechanical engineering courses, the content of laboratory exercises is not well coordinated with course content, and the exercises are unrelated to each other. As a result, students have a difficult time grasping the "big picture" themes. This project at Rice University seeks to improve the effectiveness of laboratory exercises in a required undergraduate mechanical engineering system dynamics course via student-centered learning and laboratory topics featuring haptic paddles, devices that allow users to interact via the sense of touch with virtual environments. One outcome of these improvements is a cohesive set of laboratory experiments using the haptic paddles as a single experimental test bed for multiple experiments. The Haptic Paddle exercises are unique because they allow the students to analyze and build their own haptic interface, or force-reflecting system. The students are able to see many subsets of mechanical engineering come together in a series of exercises, including assembly, system analysis, calibration, system modeling, and dynamics. Finally, a key advantage to the haptic paddle labs is that they tie closely with the course material. This paper describes the development of haptic paddle laboratory kits and associated National Instruments LabVIEW virtual instrumentation to support the adaptation of laboratory experiments for a required undergraduate system dynamics course at Rice University. The laboratory experiments use simple haptic interfaces, devices that allow the students to interact via the sense of touch with virtual environments. A clear benefit of this laboratory series is that students study the haptic paddle as a real electromechanical system in addition to using the haptic paddle as a tool to interact with virtual mechanical systems. The haptic paddle hardware has been modified to improve robustness, and the LabVIEW graphical programming language is used for data acquisition and control throughout the laboratory series. The paper will present some details of the laboratory components, and preliminary assessment of learning outcomes using this laboratory series compared to more traditional modular labs used in prior years. {\textcopyright} American Society for Engineering Education, 2006.

}, keywords = {Computer programming languages, Electromechanical devices, Engineering education, Learning systems, Mechanical engineering, Students, Virtual reality}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/36-ASEE2006-paper-O\%27Malley\%20session\%201526.pdf}, author = {Kevin Bowen and O{\textquoteright}Malley, M.K.} } @proceedings {072310640980, title = {The RiceWrist: A distal upper extremity rehabilitation robot for stroke therapy}, year = {2006}, note = {

Mirror Image Movement Enabler (MIME) system;Rehabilitation robot;Robotic therapy;

}, month = {11/2006}, pages = {10 -}, publisher = {ASME}, address = {Chicago, IL, United States}, abstract = {

This paper presents the design and kinematics of a four degree-of-freedom upper extremity rehabilitation robot for stroke therapy, to be used in conjunction with the Mirror Image Movement Enabler (MIME) system. The RiceWrist is intended to provide robotic therapy via force-feedback during range-of-motion tasks. The exoskeleton device accommodates forearm supination and pronation, wrist flexion and extension, and radial and ulnar deviation in a compact design with low friction and backlash. Joint range of motion and torque output of the electricmotor driven device is matched to human capabilities. The paper describes the design of the device, along with three control modes that allow for various methods of interaction between the patient and the robotic device. Passive, triggered, and active-constrained modes, such as those developed for MIME, allow for therapist control of therapy protocols based on patient capability and progress. Also presented is the graphical user interface for therapist control of the interactions modes of the RiceWrist, basic experimental protocol, and preliminary experimental results. Copyright {\textcopyright} 2006 by ASME.

}, keywords = {Degrees of freedom (mechanics), Graphical user interfaces, Human rehabilitation equipment, Patient treatment}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/46-00\%20-\%20IMECE2006-16103-O\%27Malley.pdf}, author = {O{\textquoteright}Malley, M.K. and Alan Sledd and Abhishek Gupta and Volkan Patoglu and Joel C. Huegel and Burgar, Charles} } @proceedings {04278244642, title = {Cooperative manipulation between humans and teleoperated agents}, year = {2004}, note = {

Robonauts;Haptic feedback;Cooperative manipulation;

}, pages = {114 - 120}, address = {Chicago, IL, United States}, abstract = {

Robonaut is a humanoid robot designed by the Robotic Systems Technology Branch at NASA{\textquoteright}s Johnson Space Center in a collaborative effort with DARPA. This paper describes the implementation of haptic feedback into Robonaut. We conducted a cooperative manipulation task, inserting a flexible beam into an instrumented receptacle. This task was performed while both a human at the worksite and the teleoperated robot grasped the flexible beam simultaneously. Peak forces in the receptacle were consistently lower when the human operator was provided with kinesthetic force feedback in addition to other modalities of feedback such as gestures and voice commands. These findings are encouraging as the Dexterous Robotics Lab continues to implement force feedback into its teleoperator hardware architecture.

}, keywords = {Computer simulation, Feedback, Haptic interfaces, Human computer interaction, Robots, Statistical methods}, url = {http://dx.doi.org/10.1109/HAPTIC.2004.1287185}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/glassmire2004haptics.pdf}, author = {John Glassmire and O{\textquoteright}Malley, M.K. and William Bluethmann and Robert O. Ambrose} }