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A knowledge-light approach to personalised and open-ended human activity recognition.

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

Authors

Anjana Wijekoon

Sadiq Sani



Abstract

Human Activity Recognition (HAR) is a core component of clinical decision support systems that rely on activity monitoring for self-management of chronic conditions such as Musculoskeletal Disorders. Deployment success of such applications in part depend on their ability to adapt to individual variations in human movement and to facilitate a range of human activity classes. Research in personalised HAR aims to learn models that are sensitive to the subtle nuances in human movement whilst Open-ended HAR learns models that can recognise activity classes out of the pre-defined set available at training. Current approaches to personalised HAR impose a data collection burden on the end user; whilst Open-ended HAR algorithms are heavily reliant on intermediary-level class descriptions. Instead of these 'knowledge-intensive' HAR algorithms; in this article, we propose a 'knowledge-light' method. Specifically, we show how by using a few seconds of raw sensor data, obtained through micro-interactions with the end-user, we can effectively personalise HAR models and transfer recognition functionality to new activities with zero re-training of the model after deployment. We introduce a Personalised Open-ended HAR algorithm, MNZ, a user context aware Matching Network architecture and evaluate on 3 HAR data sources. Performance results show up to 48.9% improvement with personalisation and up to 18.3% improvement compared to the most common 'knowledge-intensive' Open-ended HAR algorithms.

Citation

WIJEKOON, A., WIRATUNGA, N., SANI, S. and COOPER, K. 2020. A knowledge-light approach to personalised and open-ended human activity recognition. Knowledge-based systems [online], 192, article ID 105651. Available from: https://doi.org/10.1016/j.knosys.2020.105651

Journal Article Type Article
Acceptance Date Feb 9, 2020
Online Publication Date Feb 13, 2020
Publication Date Mar 15, 2020
Deposit Date Feb 11, 2020
Publicly Available Date Feb 14, 2021
Journal Knowledge-based systems
Print ISSN 0950-7051
Electronic ISSN 1872-7409
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 192
Article Number 105651
DOI https://doi.org/10.1016/j.knosys.2020.105651
Keywords Human activity recognition; Personalised HAR; Open-ended HAR; Zero-shot learning; Matching networks
Public URL https://rgu-repository.worktribe.com/output/854277

Files

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