Learning deep features for kNN-based human activity recognition.
Sani, Sadiq; Wiratunga, Nirmalie; Massie, Stewart
Professor Nirmalie Wiratunga firstname.lastname@example.org
Antonio A. Sanchez-Ruiz
A CBR approach to Human Activity Recognition (HAR) uses the kNN algorithm to classify sensor data into different activity classes. Different feature representation approaches have been proposed for sensor data for the purpose of HAR. These include shallow features, which can either be hand-crafted from the time and frequency domains, or the coefficients of frequency transformations. Alternatively, deep features can be extracted using deep learning approches. These different representation approaches have been compared in previous works without a consistent best approach being identified. In this paper, we explore the question of which representation approach is best for kNN. Accordingly, we compare 5 different feature representation approaches (ranging from shallow to deep) on accelerometer data collected from two body locations, wrist and thigh. Results show deep features to produce the best results for kNN, compared to both hand-crafted and frequency transform, by a margin of up to 6.5% on the wrist and over 2.2% on the thigh. In addition, kNN produces very good results with as little as a single epoch of training for the deep features.
|Start Date||Jun 26, 2017|
|Publication Date||Dec 18, 2017|
|Publisher||CEUR Workshop Proceedings|
|Series Title||CEUR workshop proceedings|
|Institution Citation||SANI, S., WIRATUNGA, N. and MASSIE, S. 2017. Learning deep features for kNN-based human activity recognition. In Sanchez-Ruiz, A.A. and Kofod-Petersen, A. (eds.) Workshop proceedings of the 25th International conference on case-based reasoning (ICCBR 2017), 26-29 June 2017, Trondheim, Norway. CEUR workshop proceedings, 2028. Aachen: CEUR-WS [online], session 2: case-based reasoning and deep learning workshop (CBRDL-2017), pages 95-103. Available from: http://ceur-ws.org/Vol-2028/paper9.pdf|
|Keywords||Human activity recognition; Feature representation; Deep learning|
SANI 2017 Learning deep features for kNN-based
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