Title | ||
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A knowledge-light approach to personalised and open-ended human activity recognition. |
Abstract | ||
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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, MN
Z, 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. |
Year | DOI | Venue |
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2020 | 10.1016/j.knosys.2020.105651 | Knowledge-Based Systems |
Keywords | Field | DocType |
Human activity recognition,Personalised HAR,Open-ended HAR,Zero-shot learning,Matching networks | Data collection,Software deployment,Activity recognition,End user,Computer science,Network architecture,Core component,Artificial intelligence,Clinical decision support system,Machine learning,Personalization | Journal |
Volume | ISSN | Citations |
192 | 0950-7051 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Anjana Wijekoon | 1 | 0 | 5.07 |
Nirmalie Wiratunga | 2 | 472 | 45.76 |
Sadiq Sani | 3 | 9 | 5.60 |
Kay Cooper | 4 | 3 | 2.47 |