NDLI: Musculoskeletal model predicts multi-joint wrist and hand movement from limited EMG control signals

Content Provider	IEEE Xplore Digital Library
Author	Crouch, D.L. He Huang
Copyright Year	2015
Description	Author affiliation: Dept. of Biomed. Eng., NC State Univ., Raleigh, NC, USA (Crouch, D.L.; He Huang)
Abstract	Electromyography (EMG)-driven human-machine systems permit volitional control of external devices, including powered prosthetic arms. However, current control schemes are either non-intuitive to operate or lack robustness across different arm postures and dynamics, partly because these methods did not incorporate the full knowledge of biological movement production. In this study, we developed and evaluated a new musculoskeletal model to predict hand and wrist motion based on surface EMG signals. Kinematic and EMG data were collected from an able-bodied subject while performing wrist and metacarpophalangeal (MCP) joint movements with either a fixed or random speed in two static upper limb postures. A part of data collected in one posture was used to develop the model with four virtual muscles. Four parameters were optimized for each of four muscles in one posture. The model kinematic predictions were evaluated offline using the other part of the data recorded from both postures. Mean (±SD) RMS errors in predicting the joint movement were significantly lower at the MCP joint (10.1±2.5°) than at the wrist (23.5±5.2°) (p<;0.05). At both the wrist and MCP joints, the model predicted the timing and trend of joint movements reasonably well across postures and for both simple (fixed speed, single joint) and complex (random speed, simultaneous, multi-joint) movements. The results implied that our EMG-driven musculoskeletal model was promising for predicting simultaneous joint motions without significant posture and dynamics dependency. Additional engineering efforts are still needed to improve the musculoskeletal model for various human-machine interfacing applications.
Starting Page	1132
Ending Page	1135
File Size	729689
Page Count	4
File Format	PDF
ISSN	1557170X
e-ISBN	9781424492718
DOI	10.1109/EMBC.2015.7318565
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Muscles Joints Wrist Electromyography Predictive models Biological system modeling
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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