TY - GEN
T1 - Hand Movement Discrimination Using Particle Filters
AU - Amor, N.
AU - Rasool, G.
AU - Bouaynaya, N.
AU - Shterenberg, R.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Powered prosthetic devices can be driven using task-specific information from surface electromyogram (sEMG) signals recorded over the relevant muscles. The task-specific information can be extracted from sEMG signals using the state-space framework, which models movement planning and execution by the central nervous system (CNS). The proposed state-space model consists of a nonlinear system dynamics model and a linear measurement model based on the hypothesis of muscle synergies. The unknown system state, which is to be estimated, consists of synergy activation coefficients and is constrained to be non-negative on average due to physiological reasons. To solve this constrained nonlinear estimation problem, we propose a modification to the particle filter, which first draws particles from the unconstrained posterior distribution and then enforces the constraints by sampling from a high probability region. This method is termed MEan DEnsity Truncation (MEDET) in contrast to an approach that constrains the entire posterior density. The constrained state estimates, i.e., synergy activation coefficients are later used to discriminate between six different tasks including hand open, hand close, wrist flexion, wrist extension, forearm pronation and supination of three participants. The newly proposed PF-MEDET algorithm was able to discriminate hand tasks with more than 97% accuracy.
AB - Powered prosthetic devices can be driven using task-specific information from surface electromyogram (sEMG) signals recorded over the relevant muscles. The task-specific information can be extracted from sEMG signals using the state-space framework, which models movement planning and execution by the central nervous system (CNS). The proposed state-space model consists of a nonlinear system dynamics model and a linear measurement model based on the hypothesis of muscle synergies. The unknown system state, which is to be estimated, consists of synergy activation coefficients and is constrained to be non-negative on average due to physiological reasons. To solve this constrained nonlinear estimation problem, we propose a modification to the particle filter, which first draws particles from the unconstrained posterior distribution and then enforces the constraints by sampling from a high probability region. This method is termed MEan DEnsity Truncation (MEDET) in contrast to an approach that constrains the entire posterior density. The constrained state estimates, i.e., synergy activation coefficients are later used to discriminate between six different tasks including hand open, hand close, wrist flexion, wrist extension, forearm pronation and supination of three participants. The newly proposed PF-MEDET algorithm was able to discriminate hand tasks with more than 97% accuracy.
UR - http://www.scopus.com/inward/record.url?scp=85062081979&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062081979&partnerID=8YFLogxK
U2 - 10.1109/SPMB.2018.8615592
DO - 10.1109/SPMB.2018.8615592
M3 - Conference contribution
AN - SCOPUS:85062081979
T3 - 2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018 - Proceedings
BT - 2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2018
Y2 - 1 December 2018
ER -