A proportional pattern recognition control scheme for wearable a-mode ultrasound sensing
Research output: Contribution to journal › Article › peer-review
Standard
A proportional pattern recognition control scheme for wearable a-mode ultrasound sensing. / Yang, Xingchen; Yan, Jipeng; Chen, Zhenfeng; Ding, Han; Liu, Honghai.
In: IEEE Transactions on Industrial Electronics, Vol. 67, No. 1, 8654210, 01.01.2020, p. 800-808.Research output: Contribution to journal › Article › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - A proportional pattern recognition control scheme for wearable a-mode ultrasound sensing
AU - Yang, Xingchen
AU - Yan, Jipeng
AU - Chen, Zhenfeng
AU - Ding, Han
AU - Liu, Honghai
PY - 2020/1/1
Y1 - 2020/1/1
N2 - It is evident that the prevailing solution, myoelectric pattern recognition for prosthetic manipulation, constrains gesture-based interaction because of the lack of proportional control information such as exerted force. This paper reports an attempt, named simultaneous gesture recognition and muscle contraction force estimation, to realize proportional pattern recognition (PPR) control taking advantage of arm muscle deformation via wearable ultrasound sensing. We experiment with eight types of predefined hand motions, with a range of 0-60% maximum voluntary contraction (MVC) using a wearable multichannel A-mode ultrasound system. The experiment result demonstrates that above 93.7% of gestures are correctly recognized during dynamic muscle contraction forces (0-60% MVC), albeit only training at a slight force level (<6% MVC). Besides, the adopted nonparametric Gaussian process regression estimates the muscle contraction forces accurately and synchronously, with average coefficient of determination, bf Rbf2, and normalized root-mean-square error (nRMSE) of 0.927 and 0.102, respectively. These research outcomes demonstrate the feasibility of ultrasound-based PPR control, paving the way for musculature-driven applications such as finer prosthetic control, remote manipulation, and rehabilitation treatment.
AB - It is evident that the prevailing solution, myoelectric pattern recognition for prosthetic manipulation, constrains gesture-based interaction because of the lack of proportional control information such as exerted force. This paper reports an attempt, named simultaneous gesture recognition and muscle contraction force estimation, to realize proportional pattern recognition (PPR) control taking advantage of arm muscle deformation via wearable ultrasound sensing. We experiment with eight types of predefined hand motions, with a range of 0-60% maximum voluntary contraction (MVC) using a wearable multichannel A-mode ultrasound system. The experiment result demonstrates that above 93.7% of gestures are correctly recognized during dynamic muscle contraction forces (0-60% MVC), albeit only training at a slight force level (<6% MVC). Besides, the adopted nonparametric Gaussian process regression estimates the muscle contraction forces accurately and synchronously, with average coefficient of determination, bf Rbf2, and normalized root-mean-square error (nRMSE) of 0.927 and 0.102, respectively. These research outcomes demonstrate the feasibility of ultrasound-based PPR control, paving the way for musculature-driven applications such as finer prosthetic control, remote manipulation, and rehabilitation treatment.
KW - Force estimation
KW - gesture recognition
KW - graded force control
KW - wearable ultrasound sensing
UR - http://www.scopus.com/inward/record.url?scp=85072132489&partnerID=8YFLogxK
U2 - 10.1109/TIE.2019.2898614
DO - 10.1109/TIE.2019.2898614
M3 - Article
AN - SCOPUS:85072132489
VL - 67
SP - 800
EP - 808
JO - IEEE Transactions on Industrial Electronics
JF - IEEE Transactions on Industrial Electronics
SN - 0278-0046
IS - 1
M1 - 8654210
ER -
ID: 18810070