@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 {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} } @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} } @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} } @proceedings {109, title = {Progressive shared control for training in virtual environments}, year = {2009}, month = {03/2009}, pages = {332-337}, publisher = {IEEE}, address = {Salt Lake City, UT, USA}, keywords = {Haptic interface, performance, shared control, training}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/109-LiPSC-WHC.pdf}, author = {Yanfang Li and Joel C. Huegel and Volkan Patoglu and O{\textquoteright}Malley, M.K.} } @proceedings {110, title = {Visual Versus Haptic Progressive Guidance For Training In A Virtual Dynamic Task}, year = {2009}, month = {03/2009}, publisher = {IEEE}, address = {Salt Lake City, UT, USA}, keywords = {Haptic interface, training, virtual environment}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/110-Huegel-ProgDemo-WHC.pdf}, author = {Joel C. Huegel and O{\textquoteright}Malley, M.K.} }