TY - GEN
T1 - Robotic solutions to facilitate studying human motor control
AU - Ramadan, Ahmed
AU - Choi, Jongeun
AU - Radcliffe, Clark J.
AU - Cholewicki, Jacek
AU - Peter Reeves, N.
AU - Popovich, John M.
PY - 2017/7/25
Y1 - 2017/7/25
N2 - Studying human motor control requires innovative engineering solutions including robots. For example, the motor control task(s) must be reliable before using system identification (SYSID) tools to estimate any motor control descriptors, e.g. neurophysiological parameters. Robots can improve the experiment reliability in contrast to passive devices, e.g. seated balance on a hemisphere. First, we present some novel physical human-robot interaction (pHRI) tasks in the biomechanics field. Then, we show how one pHRI can achieve excellent reliability measures. Moreover, robots can allow the use a wide variety of input/perturbation signals that may be designed specifically for better SYSID results, such as better estimation error variance. With standard signals, however, further analysis is required to improve the SYSID outcomes. Therefore, we devised an analysis method based on Fisher information to reduce the estimation error variance.
AB - Studying human motor control requires innovative engineering solutions including robots. For example, the motor control task(s) must be reliable before using system identification (SYSID) tools to estimate any motor control descriptors, e.g. neurophysiological parameters. Robots can improve the experiment reliability in contrast to passive devices, e.g. seated balance on a hemisphere. First, we present some novel physical human-robot interaction (pHRI) tasks in the biomechanics field. Then, we show how one pHRI can achieve excellent reliability measures. Moreover, robots can allow the use a wide variety of input/perturbation signals that may be designed specifically for better SYSID results, such as better estimation error variance. With standard signals, however, further analysis is required to improve the SYSID outcomes. Therefore, we devised an analysis method based on Fisher information to reduce the estimation error variance.
UR - http://www.scopus.com/inward/record.url?scp=85034241433&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85034241433&partnerID=8YFLogxK
U2 - 10.1109/URAI.2017.7992704
DO - 10.1109/URAI.2017.7992704
M3 - Conference contribution
AN - SCOPUS:85034241433
T3 - 2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2017
SP - 174
EP - 178
BT - 2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2017
Y2 - 28 June 2017 through 1 July 2017
ER -