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Adaptive coefficient-based kernelized network for personalized activity recognition
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DOI：10.1007/s13042-021-01455-w.png)
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摘要
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Human activity recognition (HAR) based on wearable devices has found wide applications in fitness, health care, etc. Given the personalized wearing styles of such devices and distinctive motion patterns, the activities of daily living normally vary from person to person in terms of strength, amplitude, speed, category, etc. The specialization of a universal HAR model to a specific subject without experiencing catastrophic forgetting is a significant challenge. In this paper, we propose a novel incremental learning method, namely, an adaptive coefficient-based kernelized and regularized network (KeRNet-AC), for personalized activity recognition. During the adaptation stage of the model training process, KeRNet-AC consistently monitors the probable ill-conditioned degree of the generated solution, which we believe is strongly correlated with the catastrophic forgetting problem, and automatically makes the solution well conditioned. To reduce the computational complexity of KeRNet-AC, we also introduce an active data selection principle into KeRNet-AC. This variation is called A-KeRNet-AC. To evaluate the performance of KeRNet-AC and A-KeRNet-AC, we conduct extensive experiments on five public activity datasets. The experimental results demonstrate that KeRNet-AC outperforms related state-of-the-art methods in most cases and that A-KeRNet-AC can quickly perform model training and activity prediction. Moreover, the performance of the proposed methods steadily improves during the adaptation stage and ultimately converges without degradation, demonstrating the strong potential of KeRNet-AC and A-KeRNet-AC for personalized activity recognition.
Keyword:
Incremental learning
Activity recognition
Neural network
Wearable device
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