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With the rapid development of wearable Internet of Things (WIoT) devices, a significant amount of sensitive/private information collected by them poses a considerable challenge to the security of the WIoT devices. The accelerometer-based gait recognition is considered as an emerging and fast-evolving technology in security and access control fields and has achieved outstanding performance at certain fixed walking speeds. However, the gait recognition performance of the above technology deteriorates dramatically when the walking speed varies. To address this issue, both the speed-adaptive gait cycle segmentation method and individualized matching threshold generation method were proposed in this paper. Furthermore, the contrast experiments were conducted on the ZJU-GaitAcc public dataset sampled from five different body locations and the self-collected dataset sampled at various walking speeds. The experimental results indicated the average gait recognition and user authentication rates of 96.9% and 91.75%, respectively. As compared to the available state-of-the-art methods based on the fixed walking speeds and constant thresholds, the proposed method improved the gait recognition by 25.8% and user authentication by 21.5%.
Sun et al. (Fri,) studied this question.