Wearable-based behaviour interpolation for semi-supervised human activity recognition

• URL: http://arxiv.org/abs/2405.15962v1
• Date: Fri, 24 May 2024 22:21:24 GMT
• Title: Wearable-based behaviour interpolation for semi-supervised human activity recognition
• Authors: Haoran Duan, Shidong Wang, Varun Ojha, Shizheng Wang, Yawen Huang, Yang Long, Rajiv Ranjan, Yefeng Zheng,
• Abstract summary: We introduce a deep semi-supervised Human Activity Recognition (HAR) approach, MixHAR, which concurrently uses labelled and unlabelled activities. Our results demonstrate that MixHAR significantly improves performance, underscoring the potential of deep semi-supervised techniques in HAR.
• Score: 27.895342617584085
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While traditional feature engineering for Human Activity Recognition (HAR) involves a trial-anderror process, deep learning has emerged as a preferred method for high-level representations of sensor-based human activities. However, most deep learning-based HAR requires a large amount of labelled data and extracting HAR features from unlabelled data for effective deep learning training remains challenging. We, therefore, introduce a deep semi-supervised HAR approach, MixHAR, which concurrently uses labelled and unlabelled activities. Our MixHAR employs a linear interpolation mechanism to blend labelled and unlabelled activities while addressing both inter- and intra-activity variability. A unique challenge identified is the activityintrusion problem during mixing, for which we propose a mixing calibration mechanism to mitigate it in the feature embedding space. Additionally, we rigorously explored and evaluated the five conventional/popular deep semi-supervised technologies on HAR, acting as the benchmark of deep semi-supervised HAR. Our results demonstrate that MixHAR significantly improves performance, underscoring the potential of deep semi-supervised techniques in HAR.

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