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Learning deep and shallow features for human activity recognition.

Sani, Sadiq; Massie, Stewart; Wiratunga, Nirmalie; Cooper, Kay

Authors

Sadiq Sani

Stewart Massie

Nirmalie Wiratunga

Kay Cooper



Contributors

Gang Li
Editor

Yong Ge
Editor

Zili Zhang
Editor

Zhi Jin
Editor

Michael Blumenstein
Editor

Abstract

selfBACK is an mHealth decision support system used by patients for the self-management of Lower Back Pain. It uses Human Activity Recognition from wearable sensors to monitor user activity in order to measure their adherence to prescribed physical activity plans. Different feature representation approaches have been proposed for Human Activity Recognition, including shallow, such as with hand-crafted time domain features and frequency transformation features; or, more recently, deep with Convolutional Neural Net approaches. The different approaches have produced mixed results in previous work and a clear winner has not been identified. This is especially the case for wrist mounted accelerometer sensors which are more susceptible to random noise compared to data from sensors mounted at other body locations e.g. thigh, waist or lower back. In this paper, we compare 7 different feature representation approaches on accelerometer data collected from both the wrist and the thigh. In particular, we evaluate a Convolutional Neural Net hybrid approach that has been shown to be effective on image retrieval but not previously applied to Human Activity Recognition. Results show the hybrid approach is effective, producing the best results compared to both hand-crafted and frequency domain feature representations by a margin of over 1.4% on the wrist.

Start Date Aug 19, 2017
Publication Date Jul 19, 2017
Print ISSN 0302-9743
Publisher Springer (part of Springer Nature)
Pages 469-482
Series Title Lecture notes in artificial intelligence
Series Number 10412
Series ISSN 0302-9743
ISBN 9783319635576
Institution Citation SANI, S., MASSIE, S., WIRATUNGA, N. and COOPER, K. 2017. Learning deep and shallow features for human activity recognition. In Li, G., Ge, Y, Zhang, Z., Jin, Z. and Blumenstein, M. (eds.) Knowledge science, engineering and management: proceedings of the 10th International knowledge science, engineering and management conference (KSEM 2017), 19-20 August 2017, Melbourne, Australia. Lecture notes in artificial intelligence, 10412. Cham: Springer [online], pages 469-482. Available from: https://doi.org/10.1007/978-3-319-63558-3_40
DOI https://doi.org/10.1007/978-3-319-63558-3_40
Keywords Human activity recognition; Feature representation; Deep learning

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