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Unsupervised adaptation for acceleration-based activity recognition: robustness to sensor displacement and rotation

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Abstract

A common assumption in activity recognition is that the system remains unchanged between its design and its posterior operation. However, many factors affect the data distribution between two different experimental sessions. One of these factors is the potential change in the sensor location (e.g. due to replacement or slippage) affecting the classification performance. Assuming that changes in the sensor placement mainly result in shifts in the feature distributions, we propose an unsupervised adaptive classifier that calibrates itself using an online version of expectation–maximisation. Tests using three activity recognition scenarios show that the proposed adaptive algorithm is robust against shift in the feature space due to sensor displacement and rotation. Moreover, since the method estimates the change in the feature distribution, it can also be used to roughly evaluate the reliability of the system during online operation.

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Acknowledgments

We would like to thank K. Förster and D. Roggen from ETH Zurich, and H. Sagha from EPFL, Lausanne for providing the experimental data and insightful discussions. This work was supported by the EU-FET project ICT-225938 (Opportunity: Activity and Context Recognition with Opportunistic Sensor Configuration). This paper only reflects the authors’ views and funding agencies are not liable for any use that may be made of the information contained herein.

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Authors and Affiliations

  1. Defitech Chair in Non-Invasive Brain-Computer Interface (CNBI), Center for Neuroprosthetics, School of Engineering, Ecole Polytechnique Fédérale de Lausanne, 1015, Lausanne, Switzerland

    Ricardo Chavarriaga, Hamidreza Bayati & José del R. Millán

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  1. Ricardo Chavarriaga
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  2. Hamidreza Bayati
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  3. José del R. Millán
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Correspondence to Ricardo Chavarriaga.

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Based on “Unsupervised adaptation to on-body sensor displacement in acceleration-based activity recognition”, by H. Bayati, J. R. Millán, and R. Chavarriaga which appeared in the Proceedings of the International Symposium on Wearable Computers, San Francisco, California, June 2011. © 2011 IEEE.

R. Chavarriaga and H. Bayati contributed equally to this work.

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Chavarriaga, R., Bayati, H. & Millán, J.R. Unsupervised adaptation for acceleration-based activity recognition: robustness to sensor displacement and rotation. Pers Ubiquit Comput 17, 479–490 (2013). https://doi.org/10.1007/s00779-011-0493-y

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  • DOI: https://doi.org/10.1007/s00779-011-0493-y

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