Dr Anjana Wijekoon a.wijekoon1@rgu.ac.uk
Research Fellow B
A knowledge-light approach to personalised and open-ended human activity recognition.
Wijekoon, Anjana; Wiratunga, Nirmalie; Sani, Sadiq; Cooper, Kay
Authors
Professor Nirmalie Wiratunga n.wiratunga@rgu.ac.uk
Associate Dean for Research
Sadiq Sani
Professor Kay Cooper k.cooper@rgu.ac.uk
Associate Dean (Research)
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 | Mar 29, 2024 |
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
WIJEKOON 2020 A knowledge-light
(3.8 Mb)
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Publisher Licence URL
https://creativecommons.org/licenses/by-nc-nd/4.0/
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