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Aggressive and agitated behavior recognition from accelerometer data using non-negative matrix factorization

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Abstract

This paper presents a novel approach for aggressive and agitated behavior recognition using accelerometer data. Our approach applies first a noise reduction technique using the moving average filter method. Then, multiple features such as mean, variance, entropy, correlation and covariance are extracted from the filtered acceleration data using a sliding window. Non-negative matrix factorization is then used in order to project the data into a new reduced space that captures the significant structure of the data. The recognition is performed using the rotation forest ensemble method. The proposed approach is validated using extensive experiments on a real dataset collected at Toronto Rehabilitation Institute. We empirically demonstrate that our proposed approach accurately discriminates between behaviors and performs better than several state-of-the-art approaches.

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Notes

  1. http://www.shimmersensing.com.

  2. http://www.shimmersensing.com/shop/shimmer3.

  3. Here we use the terms Behavior and Action interchangeably.

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Authors and Affiliations

  1. TELUQ University, Montreal, Canada

    Belkacem Chikhaoui

  2. IATSL Laboratory, Toronto Rehab Institute, University of Toronto, Toronto, Canada

    Bing Ye & Alex Mihailidis

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  1. Belkacem Chikhaoui
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  2. Bing Ye
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  3. Alex Mihailidis
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Correspondence to Belkacem Chikhaoui.

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Chikhaoui, B., Ye, B. & Mihailidis, A. Aggressive and agitated behavior recognition from accelerometer data using non-negative matrix factorization. J Ambient Intell Human Comput 9, 1375–1389 (2018). https://doi.org/10.1007/s12652-017-0537-x

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