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Improving kNN for human activity recognition with privileged learning using translation models.

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

Authors

Anjana Wijekoon

Sadiq Sani



Contributors

Michael T. Cox
Editor

Peter Funk
Editor

Shahina Begum
Editor

Abstract

Multiple sensor modalities provide more accurate Human Activity Recognition (HAR) compared to using a single modality, yet the latter is preferred by consumers as it is more convenient and less intrusive. This presents a challenge to researchers, as a single modality is likely to pick up movement that is both relevant as well as extraneous to the human activity being tracked and lead to poorer performance. The goal of an optimal HAR solution is therefore to utilise the fewest sensors at deployment, while maintaining performance levels achievable using all available sensors. To this end, we introduce two translation approaches, capable of generating missing modalities from available modalities. These can be used to generate missing or 'privileged' modalities at deployment to augment case representations and improve HAR.We evaluate the presented translators with k-NN classifiers on two HAR datasets and achieve up-to 5% performance improvements using representations augmented with privileged modalities. This suggests that non-intrusive modalities suited for deployment benefit from translation models that generates privileged modalities.

Start Date Jul 9, 2018
Publication Date Nov 8, 2018
Print ISSN 0302-9743
Electronic ISSN 1611-3349
Publisher Springer (part of Springer Nature)
Pages 448-463
Series Title Lecture notes in computer science
Series Number 11156
Series ISSN 1611-3349
ISBN 9783030010805
Institution Citation WIJEKOON, A., WIRATUNGA, N., SANI, S., MASSIE, S. and COOPER, K. 2018. Improving kNN for human activity recognition with privileged learning using translation models. In Cox, M.T., Funk, P. and Begum, S. (eds.) Case-based reasoning research and development: proceedings of the 26th International conference on case-based reasoning (ICCBR 2018), 9-12 July 2018, Stockholm, Sweden. Lecture notes in computer science, 11156. Cham: Springer [online], pages 448-463. Available from: https://doi.org/10.1007/978-3-030-01081-2_30
DOI https://doi.org/10.1007/978-3-030-01081-2_30
Keywords Human activity recognition; Machine learning; Case representation; Privileged learning

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