@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} } @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 {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 {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} } @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} } @article {1853, title = {Trajectory deformations from physical human{\textendash}robot interaction}, journal = {IEEE Transactions on Robotics}, volume = {34}, number = {1}, year = {2018}, month = {02/2018}, pages = {126-138}, abstract = {

Robots are finding new applications where physical interaction with a human is necessary, such as manufacturing, healthcare, and social tasks. Accordingly, the field of physical human{\textendash}robot interaction (pHRI) has leveraged impedance control approaches, which support compliant interactions between human and robot. However, a limitation of traditional impedance control is that{\textemdash}despite provisions for the human to modify the robot{\textquoteright}s current trajectory{\textemdash}the human cannot affect the robot{\textquoteright}s future desired trajectory through pHRI. In this paper, we present an algorithm for physically interactive trajectory deformations which, when combined with impedance control, allows the human to modulate both the actual and desired trajectories of the robot. Unlike related works, our method explicitly deforms the future desired trajectory based on forces applied during pHRI, but does not require constant human guidance. We present our approach and verify that this method is compatible with traditional impedance control. Next, we use constrained optimization to derive the deformation shape. Finally, we describe an algorithm for real-time implementation, and perform simulations to test the arbitration parameters. Experimental results demonstrate reduction in the human{\textquoteright}s effort and improvement in the movement quality when compared to pHRI with impedance control alone.

}, doi = {10.1109/TRO.2017.2765335}, url = {http://ieeexplore.ieee.org/document/8115323/}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Losey_TRO_2018.pdf}, author = {Dylan P. Losey 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 {1829, title = {A Time-Domain Approach To Control Of Series Elastic Actuators: Adaptive Torque And Passivity-Based Impedance Control}, journal = {IEEE/ASME Transactions on Mechatronics}, volume = {21}, number = {4}, year = {2016}, pages = {2085 - 2096}, abstract = {

Robots are increasingly designed to physically interact with humans in unstructured environments, and as such must operate both accurately and safely. Leveraging compliant actuation, typically in the form of series elastic actuators (SEAs), can guarantee this required level of safety. To date, a number of frequency-domain techniques have been proposed which yield effective SEA torque and impedance control; however, these methods are accompanied by undesirable stability constraints. In this paper, we instead focus on a time-domain approach to the control of SEAs, and adapt two existing control techniques for SEA platforms. First, a model reference adaptive controller is developed, which requires no prior knowledge of system parameters and can specify desired closed-loop torque characteristics. Second, the time-domain passivity approach is modified to control desired impedances in a manner that temporarily allows the SEA to passively render impedances greater than the actuator{\textquoteright}s intrinsic stiffness. This approach also provides conditions for passivity when augmenting any stable SEA torque controller with an arbitrary impedance. The resultant techniques are experimentally validated on a custom prototype SEA.

}, issn = {1083-4435}, doi = {10.1109/TMECH.2016.2557727}, url = {http://ieeexplore.ieee.org/abstract/document/7457670/}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Losey_TMECH.pdf}, author = {Dylan P. Losey and Andrew Erwin and Craig G. McDonald and Fabrizio Sergi and Marcia K. O{\textquoteright}Malley} } @article {1787, title = {Transcranial direct current stimulation (tDCS) of the primary motor cortex and robot-assisted arm training in chronic incomplete cervical spinal cord injury: A proof of concept sham-randomized clinical study}, journal = {NeuroRehabilitation}, volume = {39}, year = {2016}, pages = {401{\textendash}411}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/TDCS_2016_Neurorehab.pdf}, author = {Nuray Yozbatirana and Zafer Keser and Matthew Davis and Argyrios Stampas and Marcia K. O{\textquoteright}Malley and Catherine Cooper-Hay and Joel Fronteraa and Felipe Fregni and Gerard E. Francisco} } @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.} } @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 {powell_task, title = {The Task-Dependent Efficacy of Shared-Control Haptic Guidance Paradigms}, journal = {{IEEE} Transactions on Haptics}, volume = {5}, number = {3}, year = {2012}, pages = {208 {\textendash}219}, abstract = {

Shared-control haptic guidance is a common form of robot-mediated training used to teach novice subjects to perform dynamic tasks. Shared-control guidance is distinct from more traditional guidance controllers, such as virtual fixtures, in that it provides novices with real-time visual and haptic feedback from a real or virtual expert. Previous studies have shown varying levels of training efficacy using shared-control guidance paradigms; it is hypothesized that these mixed results are due to interactions between specific guidance implementations ( {amp;\#x201C;paradigms} {amp;\#x201D;)} and tasks. This work proposes a novel guidance paradigm taxonomy intended to help classify and compare the multitude of implementations in the literature, as well as a revised proxy rendering model to allow for the implementation of more complex guidance paradigms. The efficacies of four common paradigms are compared in a controlled study with 50 healthy subjects and two dynamic tasks. The results show that guidance paradigms must be matched to a task{\textquoteright}s dynamic characteristics to elicit effective training and low workload. Based on these results, we provide suggestions for the future development of improved haptic guidance paradigms.

}, issn = {1939-1412}, doi = {10.1109/TOH.2012.40}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/J8Powell2012.pdf}, author = {Powell, Dane and Marcia K. O{\textquoteright}Malley} } @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 {073510787453, title = {Towards just noticeable differences for natural frequency of manually excited virtual dynamic systems}, year = {2007}, note = {

Graphical displays;Virtual mass spring systems;Dynamic properties;

}, month = {03/2007}, pages = {569 - 570}, publisher = {IEEE}, address = {Tsukuba, Japan}, abstract = {

This paper explores the experiment design to determine a human{\textquoteright}s ability to discriminate the natural frequency of manually excited virtual dynamic systems. We use a one degree-of-freedom haptic interface with a coupled graphical display to render a virtual mass-spring system, which is excited by the human operator using his/her dominant hand. The results from the preliminary experiment indicate a JND value of approximately 8\%. However, results also indicate that excitation strategies have a significant effect on the discrimination threshold determination of this dynamic property. In this paper, along with a discussion of the preliminary results, a refined experiment design that accounts for different factors influencing the discrimination of manually excited natural frequency is presented. {\textcopyright} 2007 IEEE.

}, keywords = {Display devices, Dynamical systems, Natural frequencies}, doi = {10.1109/WHC.2007.118}, url = {http://dx.doi.org/10.1109/WHC.2007.118}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/59-00\%20-\%20Towards\%20Just\%20Noticeable\%20Differences\%20for\%20Natural\%20Frequency\%20of\%20Manually\%20Excited\%20Virtual\%20Dynamic\%20Systems.pdf}, author = {Yanfang Li and Volkan Patoglu and Huang, Deborah and O{\textquoteright}Malley, M.K.} } @proceedings {06169823659, title = {Transparency extension in haptic interfaces via adaptive dynamics cancellation}, volume = {74 DSC}, number = {2 PART B}, year = {2005}, note = {

Transparency extension;Robotic manipulators;Transparency transfer function (TTF);Model cancellation techniques;

}, pages = {1581 - 1587}, address = {Orlando, FL, United States}, abstract = {

Haptic interfaces are a class of robotic manipulators that display force feedback to enhance the realism of virtual environment displays. However, these manipulators often fail to effectively replicate the real world environment due to dynamic limitations of the manipulator itself. The ratio of the simulated to transmitted environment impedance is defined as the transparency transfer function (TTF), and can be used to quantify the effectiveness of a haptic device in displaying the simulated environment. The TTF is ideally equal to one for the bandwidth of human proprioception. In this work, a method is presented that increases TTF bandwidth via cancellation of dynamics with an adaptive model. This adaptive model is based on the kinematics and dynamics of a PHANToM haptic interface with assumed joint stiffness and damping added. The Lagrangian of the PHANToM is reformulated into a regressor matrix containing the state variables multiplied by a parameter vector. A least-squares approach is used to estimate the parameter vector by assuming that errors in force output are due to the manipulator dynamics. The parameter estimate is then used in the original model to provide a feed-forward cancellation of the manipulator dynamics. Software simulation using data from passive user interactions shows that the model cancellation technique improves bandwidth up to 35 Hz versus 7 Hz without compensation. Finally, this method has a distinct advantage when compared with other compensation methodsfor haptic interactions because it does not rely on linear assumptions near a particular operating point and will adapt to capture unmodeled features. Copyright {\textcopyright} 2005 by ASME.

}, keywords = {Adaptive control systems, Computer simulation, Linear systems, Manipulators, Mathematical models, Transfer functions}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/mcjunkin2005asme.pdf}, author = {McJunkin, Samuel and Speich, John E. and O{\textquoteright}Malley, M.K.} } @proceedings {05359336514, title = {Transparency of a phantom premium haptic interface for active and passive human interaction}, volume = {5}, year = {2005}, note = {

Active user induced (AUI);Phantom manipulators;Human operators;

}, pages = {3060 - 3065}, address = {Portland, OR, United States}, abstract = {

This paper compares two methods for determining the transparency bandwidth of an impedance based haptic interface with a Phantom 1.0A haptic device. Active user induced (AUI) interaction tests, where the system excitation is generated by a human user, show that transparency bandwidth is limited to approximately 2 Hz. Passive user induced (PUI) interaction tests, where the system excitation is generated by the haptic device with a passive human operator, show that bandwidth can extend up to 50 Hz. Experimental results show that the apparent bandwidth limitations for the AUI interaction tests are dependent on the human user{\textquoteright}s inability to excite higher frequencies. Consequently, this measurement approach is insufficient for determining system bandwidth of the human operator-haptic interface system. Furthermore, data seem to indicate that there is no appreciable difference in the ability of the Phantom manipulator to display environmental impedances in either AUI or PUI interactions regardless of the user. {\textcopyright} 2005 AACC.

}, keywords = {Acoustic impedance, Bandwidth, Manipulators}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/60-Full\%20Text.pdf}, author = {McJunkin, Samuel and O{\textquoteright}Malley, M.K. and Speich, John E.} }