Pervasive monitoring of motion and muscle activation: inertial and mechanomyography fusion

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Title: Pervasive monitoring of motion and muscle activation: inertial and mechanomyography fusion
Author(s): Woodward, R
Shefelbine, S
Vaidyanathan, R
Item Type: Journal Article
Abstract: Muscle activity and human motion are useful pa- rameters to map the diagnosis, treatment, and rehabilitation of neurological and movement disorders. In laboratory and clinical environments, electromyography (EMG) and motion capture systems enable the collection of accurate, high resolution data on human movement and corresponding muscle activity. However, controlled surroundings limit both the length of time and the breadth of activities that can be measured. Features of movement, critical to understanding patient progress, can change during the course of a day and daily activities may not correlate to the limited motions examined in a laboratory. We introduce a system to measure motion and muscle activity simultaneously over the course of a day in an uncontrolled environment with minimal preparation time and ease of implementation that enables daily usage. Our system combines a bespoke inertial measurement unit (IMU) and mechanomyography (MMG) sensor, which measures the mechanical signal of muscular activity. The IMU can collect data continuously, and transmit wirelessly, for up to 10 hours. We describe the hardware design and validation and outline the data analysis (including data processing and activity classification algorithms) for the sensing system. Furthermore, we present two pilot studies to demonstrate utility of the system, including activity identification in six able-bodied subjects with an accuracy of 98%, and monitoring motion/muscle changes in a subject with cerebral palsy and of a single leg amputee over extended periods ( ∼ 5 hours). We believe these results provide a foundation for mapping human muscle activity and corresponding motion changes over time, providing a basis for a range of novel rehabilitation therapies.
Publication Date: 18-Jul-2017
Date of Acceptance: 10-May-2017
URI: http://hdl.handle.net/10044/1/48515
DOI: https://dx.doi.org/10.1109/TMECH.2017.2715163
ISSN: 1083-4435
Publisher: IEEE
Start Page: 2022
End Page: 2033
Journal / Book Title: IEEE/ASME Transactions on Mechatronics
Volume: 22
Issue: 5
Copyright Statement: This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/.
Sponsor/Funder: Engineering and Physical Sciences Research Council
Funder's Grant Number: EP/F01869X
Keywords: 0906 Electrical And Electronic Engineering
0910 Manufacturing Engineering
0913 Mechanical Engineering
Industrial Engineering & Automation
Publication Status: Published online
Appears in Collections:Faculty of Engineering
Mechanical Engineering



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