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, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\bf R}^{\bf {2}}$</tex-math></inline-formula> , and normalized root-mean-square error ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$nRMSE$</tex-math></inline-formula> ) 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.
Year
DOI
Venue
2020
10.1109/tie.2019.2898614
IEEE Transactions on Industrial Electronics
Keywords
Field
DocType
Ultrasonic imaging,Force,Muscles,Estimation,Sensors,Proportional control,Gesture recognition
Muscle contraction,Normalization (statistics),Pattern recognition,Proportional control,Gesture,Wearable computer,Gesture recognition,Artificial intelligence,Engineering,Maximum voluntary contraction,Ultrasound
Journal
Volume
Issue
ISSN
67
1
0278-0046
Citations 
PageRank 
References 
2
0.43
0
Authors
5
Name
Order
Citations
PageRank
Xingchen Yang122.46
Ji Peng Yan253.19
Zhenfeng Chen320.77
Han Ding449978.16
Honghai Liu51974178.69