Sadiq Sani
kNN sampling for personalised human recognition.
Sani, Sadiq; Wiratunga, Nirmalie; Massie, Stewart; Cooper, Kay
Authors
Professor Nirmalie Wiratunga n.wiratunga@rgu.ac.uk
Associate Dean for Research
Dr Stewart Massie s.massie@rgu.ac.uk
Associate Professor
Professor Kay Cooper k.cooper@rgu.ac.uk
Associate Dean (Research)
Contributors
David W. Aha
Editor
Jean Lieber
Editor
Abstract
The need to adhere to recommended physical activity guidelines for a variety of chronic disorders calls for high precision Human Activity Recognition (HAR) systems. In the SelfBACK system, HAR is used to monitor activity types and intensities to enable self-management of low back pain (LBP). HAR is typically modelled as a classification task where sensor data associated with activity labels are used to train a classifier to predict future occurrences of those activities. An important consideration in HAR 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 that using personalised data results in more accurate predictions. However, from a practical perspective, collecting sufficient training data from the end user may not be feasible. This has made using subject-independent data by far the more common approach in commercial HAR systems. In this paper, we introduce a novel approach which uses nearest neighbour similarity to identify examples from a subject-independent training set that are most similar to sample data obtained from the target user and uses these examples to generate a personalised model for the user. This nearest neighbour sampling approach enables us to avoid much of the practical limitations associated with training a classifier exclusively with user data, while still achieving the benefit of personalisation. Evaluations show our approach to significantly out perform a general subject-independent model by up to 5%.
Citation
SANI, S., WIRATUNGA, N., MASSIE, S. and COOPER, K. 2017. kNN sampling for personalised human recognition. In Aha, D.W. and Lieber, J. (eds.) Case-based reasoning research and development: proceedings of the 25th International case-based reasoning conference (ICCBR 2017), 26-28 June 2017, Trondheim, Norway. Lecture notes in computer science, 10339. Cham: Springer [online], pages 330-344. Available from: https://doi.org/10.1007/978-3-319-61030-6_23
Conference Name | 25th International case-based reasoning conference (ICCBR 2017) |
---|---|
Conference Location | Trondheim, Norway |
Start Date | Jun 26, 2017 |
End Date | Jun 28, 2017 |
Acceptance Date | Apr 12, 2017 |
Online Publication Date | Jun 21, 2017 |
Publication Date | Jun 21, 2017 |
Deposit Date | Sep 1, 2017 |
Publicly Available Date | Jun 22, 2018 |
Print ISSN | 0302-9743 |
Publisher | Springer |
Pages | 330-344 |
Series Title | Lecture notes in computer science |
Series Number | 10339 |
Series ISSN | 0302-9743 |
ISBN | 9783319610290 |
DOI | https://doi.org/10.1007/978-3-319-61030-6_23 |
Keywords | Physical activity; Chronic disorders; Human activity recognition (HAR); Low back pain |
Public URL | http://hdl.handle.net/10059/2486 |
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Publisher Licence URL
https://creativecommons.org/licenses/by-nc/4.0/
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