Wearable-based behaviour interpolation for semi-supervised human activity recognition
<|reference_start|>Wearable-based behaviour interpolation for semi-supervised human activity recognition: 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.<|reference_end|>
@article{duan2024wearable-based,
    title={Wearable-based behaviour interpolation for semi-supervised human
  activity recognition},
    author={Haoran Duan, Shidong Wang, Varun Ojha, Shizheng Wang, Yawen Huang,
  Yang Long, Rajiv Ranjan, Yefeng Zheng},
    journal={arXiv preprint arXiv:2405.15962},
    year={2024},
    doi={10.1016/j.ins.2024.120393},
    archivePrefix={arXiv},
    eprint={2405.15962},
    primaryClass={cs.CV}
}