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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

Yang, X, Yan, J, Chen, Z, Ding, H & Liu, H 2020, 'A proportional pattern recognition control scheme for wearable a-mode ultrasound sensing', IEEE Transactions on Industrial Electronics, vol. 67, no. 1, 8654210, pp. 800-808. https://doi.org/10.1109/TIE.2019.2898614

APA

Yang, X., Yan, J., Chen, Z., Ding, H., & Liu, H. (2020). A proportional pattern recognition control scheme for wearable a-mode ultrasound sensing. IEEE Transactions on Industrial Electronics, 67(1), 800-808. [8654210]. https://doi.org/10.1109/TIE.2019.2898614

Vancouver

Yang X, Yan J, Chen Z, Ding H, Liu H. A proportional pattern recognition control scheme for wearable a-mode ultrasound sensing. IEEE Transactions on Industrial Electronics. 2020 Jan 1;67(1):800-808. 8654210. https://doi.org/10.1109/TIE.2019.2898614

Author

Yang, Xingchen ; Yan, Jipeng ; Chen, Zhenfeng ; Ding, Han ; Liu, Honghai. / A proportional pattern recognition control scheme for wearable a-mode ultrasound sensing. In: IEEE Transactions on Industrial Electronics. 2020 ; Vol. 67, No. 1. pp. 800-808.

Bibtex

@article{bf5c52f57006459bb72c995bf4488a89,
title = "A proportional pattern recognition control scheme for wearable a-mode ultrasound sensing",
abstract = "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.",
keywords = "Force estimation, gesture recognition, graded force control, wearable ultrasound sensing",
author = "Xingchen Yang and Jipeng Yan and Zhenfeng Chen and Han Ding and Honghai Liu",
year = "2020",
month = jan,
day = "1",
doi = "10.1109/TIE.2019.2898614",
language = "English",
volume = "67",
pages = "800--808",
journal = "IEEE Transactions on Industrial Electronics",
issn = "0278-0046",
publisher = "IEEE Industrial Electronics Society",
number = "1",

}

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