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Learning Deep and Shallow Features for Human Activity Recognition

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Knowledge Science, Engineering and Management (KSEM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10412))

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.

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Notes

  1. 1.

    http://www.selfback.net/.

  2. 2.

    http://axivity.com/product/ax3.

  3. 3.

    https://github.com/rgu-selfback/activity-recognition.

  4. 4.

    Significance is tested with a two-tailed student’s t-test at p = 0.05.

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Authors and Affiliations

  1. School of Computing Science and Digital Media, Robert Gordon University, Aberdeen, AB25 1HG, Scotland, UK

    Sadiq Sani, Stewart Massie & Nirmalie Wiratunga

  2. School of Health Sciences, Robert Gordon University, Aberdeen, Scotland, UK

    Kay Cooper

Authors
  1. Sadiq Sani
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  2. Stewart Massie
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  3. Nirmalie Wiratunga
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  4. Kay Cooper
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Corresponding author

Correspondence to Stewart Massie .

Editor information

Editors and Affiliations

  1. Deakin University, Burwood, Victoria, Australia

    Gang Li

  2. University of Arizona, Tucson, Arizona, USA

    Yong Ge

  3. Southwest University, Chongqing, China

    Zili Zhang

  4. Peking University, Beijing, China

    Zhi Jin

  5. University of Technology Sydney, Sydney, New South Wales, Australia

    Michael Blumenstein

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Sani, S., Massie, S., Wiratunga, N., Cooper, K. (2017). Learning Deep and Shallow Features for Human Activity Recognition. In: Li, G., Ge, Y., Zhang, Z., Jin, Z., Blumenstein, M. (eds) Knowledge Science, Engineering and Management. KSEM 2017. Lecture Notes in Computer Science(), vol 10412. Springer, Cham. https://doi.org/10.1007/978-3-319-63558-3_40

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  • DOI: https://doi.org/10.1007/978-3-319-63558-3_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63557-6

  • Online ISBN: 978-3-319-63558-3

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