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Matching networks for personalised human activity recognition.

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

Authors

Sadiq Sani



Contributors

Isabelle Bichindaritz
Editor

Christian Guttmann
Editor

Pau Herrero
Editor

Fernando Koch
Editor

Andrew Koster
Editor

Richard Lenz
Editor

Beatriz López Ibáñez
Editor

Cindy Marling
Editor

Clare Martin
Editor

Sara Montagna
Editor

Stefania Montani
Editor

Manfred Reichert
Editor

David Riaño
Editor

Michael I. Schumacher
Editor

Annette ten Teije
Editor

Abstract

Human Activity Recognition (HAR) has many important applications in health care which include management of chronic conditions and patient rehabilitation. An important consideration when training HAR models is whether to use training data from a general population (subject-independent), or personalised training data from the target user (subject-dependent). Previous evaluations have shown personalised training to be more accurate because of the ability of resulting models to better capture individual users' activity patterns. However, collecting sufficient training data from end users may not be feasible for real-world applications. In this paper, we introduce a novel approach to personalised HAR using a neural network architecture called a matching network. Matching networks perform nearest-neighbour classification by reusing the class label of the most similar instances in a provided support set. Evaluations show our approach to substantially out perform general subject-independent models by more than 5% macro-averaged F1 score.

Start Date Jul 13, 2018
Publication Date Jul 23, 2018
Print ISSN 1613-0073
Publisher CEUR Workshop Proceedings
Pages 61-64
Series Title CEUR workshop proceedings
Series Number 2142
Series ISSN 1613-0073
Institution Citation SANI, S., WIRATUNGA, N., MASSIE, S. and COOPER, K. 2018. Matching networks for personalised human activity recognition. In Bichindaritz, I., Guttmann, C., Herrero, P., Koch, F., Koster, A., Lenz, R., López Ibáñez, B., Marling, C., Martin, C., Montagna, S., Montani, S., Reichert, M., Riaño, D., Schumacher, M.I., ten Teije, A. and Wiratunga, N. (eds.) Proceedings of the 1st Joint workshop on artificial intelligence in health, organized as part of the Federated AI meeting (FAIM 2018), co-located with the 17th International conference on autonomous agents and multiagent systems (AAMAS 2018), the 35th International conference on machine learning (ICML 2018), the 27th International joint conference on artificial intelligence (IJCAI 2018), and the 26th International conference on case-based reasoning (ICCBR 2018), 13-19 July 2018, Stockholm, Sweden. CEUR workshop proceedings, 2142. Aachen: CEUR-WS [online], pages 61-64. Available from: http://ceur-ws.org/Vol-2142/short4.pdf
Keywords Human activity recognition; Health care; Management of chronic conditions; SelfBACK project; Matching networks
Publisher URL http://ceur-ws.org/Vol-2142/short4.pdf

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