@article {JUMET2023100059, title = {Fluidically programmed wearable haptic textiles}, journal = {Device}, year = {2023}, pages = {100059}, abstract = {

Summary Haptic feedback offers a useful mode of communication in visually or auditorily noisy environments. The adoption of haptic devices in our everyday lives, however, remains limited, motivating research on haptic wearables constructed from materials that enable comfortable and lightweight form factors. Textiles, a material class fitting these needs and already ubiquitous in clothing, have begun to be used in haptics, but reliance on arrays of electromechanical controllers detracts from the benefits that textiles offer. Here, we mitigate the requirement for bulky hardware by developing a class of wearable haptic textiles capable of delivering high-resolution information on the basis of embedded fluidic programming. The designs of these haptic textiles enable tailorable amplitudinal, spatial, and temporal control. Combining these capabilities, we demonstrate wearables that deliver spatiotemporal cues in four directions with an average user accuracy of 87\%. Subsequent demonstrations of washability, repairability, and utility for navigational tasks exemplify the capabilities of our approach.

}, keywords = {analog control, fluidic control, haptic sleeve, human-machine interaction, human-robot interaction, Navigation, point force, smart textiles, spatiotemporal haptics, tactile cues}, issn = {2666-9986}, doi = {https://doi.org/10.1016/j.device.2023.100059}, url = {https://www.sciencedirect.com/science/article/pii/S2666998623000832}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/DeviceJumet2023.pdf}, author = {Barclay Jumet and Zane A. Zook and Anas Yousaf and Anoop Rajappan and Doris Xu and Te Faye Yap and Nathaniel Fino and Zhen Liu and Marcia K. O{\textquoteright}Malley and Daniel J. Preston} } @article {fino2023tactor, title = {Mechanofluidic Instability-Driven Wearable Textile Vibrotactor}, journal = {IEEE Transactions on Haptics}, year = {2023}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/IEEE_ToH_2023_Fino_wearabletactor.pdf}, author = {Fino, Nathaniel and Jumet, Barclay and Zook, Zane A and Preston, Daniel J and O{\textquoteright}Malley, Marcia K} } @proceedings {2054, title = {A Soft Approach to Convey Vibrotactile Feedback in Wearables Through Mechanical Hysteresis}, year = {2023}, publisher = {IEEE}, doi = {10.1109/RoboSoft55895.2023.10122072}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Fino_2023_Robosoft.pdf}, author = {Fino, Nathaniel and Zook, Zane A and Jumet, Barclay and Preston, Daniel J 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} } @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.} } @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 {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 {1815, title = {Quantitative testing of fMRI-compatibility of an electrically active mechatronic device for robot-assisted sensorimotor protocols}, journal = {IEEE Transactions on Biomedical Engineering}, volume = {65}, number = {7}, year = {2018}, pages = {1595-1606}, doi = {10.1109/TBME.2017.2741346}, url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8012485\&tag=1}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Farrens2018\%20-\%20Quantitative\%20testing\%20fMRI-comp.pdf}, author = {Farrens, A.J. and Zonnino, A. and Erwin,Andrew and O{\textquoteright}Malley, M.K. and Johnson, C.L. and Ress, D. and Fabrizio Sergi} } @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 {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 {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} } @proceedings {1875, title = {A Fully Automated System for the Preparation of Samples for Cryo-Electron Microscopy}, year = {2010}, publisher = {American Society of Mechanical Engineers}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/thompson2010asme.pdf}, author = {Thompson, Zachary J and Johnson, Kevin L and Overby, Nicolas and Chidi, Jessica I and Pryor, William K and O{\textquoteright}Malley, Marcia K} }