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The potential of electromyography to aid personal navigation

Pinchin, James; Smith, Gavin; Hill, Chris; Moore, Terry; Loram, Ian

Authors

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JAMES PINCHIN JAMES.PINCHIN@NOTTINGHAM.AC.UK
Transitional Assistant Professor

GAVIN SMITH GAVIN.SMITH@NOTTINGHAM.AC.UK
Assistant Professor in Business Analytic

Chris Hill

Terry Moore

Ian Loram



Abstract

This paper reports on research to explore the potential for using electromyography (EMG) measurements in pedestrian navigation. The aim is to investigate whether the relationship between human motion and the activity of skeletal muscles in the leg might be used to aid other positioning sensors, or even to determine independently the path taken by a pedestrian. The paper describes an exercise to collect sample EMG data alongside leg motion data, and the subsequent analysis of this data set using machine learning techniques to infer motion from a set of EMG sensors. The sample data set included measurements from multiple EMG sensors, a camera-based motion tracking system and a foot mounted inertial sensor. The camera based motion tracking system at MMU allowed many targets on the subjects lower body to be tracked in a small (3m x 3m x 3m) volume to millimetre accuracy. Processing the data revealed a strong, but not trivial, relation-ship between leg muscle activity and motion. Each type of motion involves many different muscles, and it is not possible to conclude merely from the triggering of any single muscle that a particular type of motion has occurred. For instance, a similar set of leg muscles is involved in both forward and backward steps. It is the precise sequencing, duration and magnitude of multiple muscle activity that allows us to determine what type of motion has occurred. Preliminary analyses of the data suggest that subsets of the EMG sensors can be used to distinguish, for instance, forward motion from backward motion, and it is expected that further analysis will reveal additional correlations that will be useful in inferring the subjects motion in more detail. This paper will introduce the EMG personal navigation con-cept, describe the data collected, explore the machine learning techniques applied to the dataset, and present the results of the analysis.

Publication Date Sep 8, 2014
Peer Reviewed Peer Reviewed
APA6 Citation Pinchin, J., Smith, G., Hill, C., Moore, T., & Loram, I. (2014). The potential of electromyography to aid personal navigation
Keywords EMG; Physiology; Indoor location
Publisher URL http://www.ion.org/publications/abstract.cfm?jp=p&articleID=12311
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information Published in: Proceedings of the 27th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2014), Tampa, Florida, September 2014, pp. 1609-1615.

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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