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kNN sampling for personalised human recognition.

Sani, Sadiq; Wiratunga, Nirmalie; Massie, Stewart; Cooper, Kay


Sadiq Sani


David W. Aha

Jean Lieber


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%.


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:

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
Keywords Physical activity; Chronic disorders; Human activity recognition (HAR); Low back pain
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