Personalised meta-learning for human activity recognition with few-data.

Wijekoon, Anjana; Wiratunga, Nirmalie

Abstract

State-of-the-art methods of Human Activity Recognition(HAR) rely on a considerable amount of labelled data to train deep architectures. This becomes prohibitive when tasked with creating models that are sensitive to personal nuances in human movement, explicitly present when performing exercises and when it is infeasible to collect training data to cover the whole target population. Accordingly, learning personalised models with few data remains an open challenge in HAR research. We present a meta-learning methodology for learning-to-learn personalised models for HAR; with the expectation that the end-user only need to provide a few labelled data. These personalised HAR models benefit from the rapid adaptation of a generic meta-model using provided few end-user data. We implement the personalised meta-learning methodology with two algorithms, Personalised MAML and Personalised Relation Networks. A comparative study shows significant performance improvements against state-of-the-art deep learning algorithms and other personalisation algorithms in multiple HAR domains. Also, we show how personalisation improved meta-model training, to learn a generic meta-model suited for a wider population while using a shallow parametric model.

Citation

WIJEKOON, A. and WIRATUNGA, N. 2020. Personalised meta-learning for human activity recognition with few-data. In Bramer, M. and Ellis, R. (eds.) Artificial intelligence XXXVII: proceedings of 40th British Computer Society's Specialist Group on Artificial Intelligence (SGAI) Artificial intelligence international conference 2020 (AI-2020), 15-17 December 2020, [virtual event]. Lecture notes in artificial intelligence, 12498. Cham: Springer [online], pages 79-93. Available from: https://doi.org/10.1007/978-3-030-63799-6_6

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 40th British Computer Society's Specialist Group on Artificial Intelligence (SGAI) Artificial intelligence international conference 2020 (AI-2020)
• Start Date: Dec 15, 2020
• End Date: Dec 17, 2020
• Acceptance Date: Sep 3, 2020
• Online Publication Date: Dec 8, 2020
• Publication Date: Dec 31, 2020
• Deposit Date: Sep 18, 2020
• Publicly Available Date: Sep 18, 2020
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 12498
• Pages: 79-93
• Series Title: Lecture notes in artificial intelligence
• Series ISSN: 0302-9743
• Book Title: Artificial intelligence XXXVII: proceedings of 40th SGAI Artificial intelligence international conference (AI 2020), 15-17 December 2020, Cambridge, UK
• ISBN: 9783030637989
• DOI: https://doi.org/10.1007/978-3-030-63799-6_6
• Keywords: Personalisation; Human activity recognition; Meta-learning; Few-shot learning
• Public URL: https://rgu-repository.worktribe.com/output/968384