利用强化学习方法转换可穿戴传感器数据,在人类活动识别中实现稳健的特征选择。

IF 1.7 4区 医学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Ravi Kumar Athota, D Sumathi
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Transforming wearable sensor data for robust feature selection in human activity recognition using reinforcement learning approach.

The practical applications of body sensor data in smart healthcare systems have drawn a lot of attention from researchers studying healthcare. Current models have trouble capturing and classifying data, especially when massive datasets are involved. This study makes use of time-sequential data and the deep reinforcement learning technique known as Generative Actor-Critic (GAC). Wearable sensor data collection makes feature selection easier by enhancing inter-class differences and decreasing intra-class variations. For robust activity modeling, deep reinforcement learning and cyclic Generative Adversarial Networks are integrated with GAC and strong temporal-sequential features. This method outperforms traditional deep learning techniques in achieving accurate recognition despite noise, with accuracy of 98.76% on UCI-HAR and 98.84 % on Motion Sense datasets.

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来源期刊
CiteScore
4.10
自引率
6.20%
发文量
179
审稿时长
4-8 weeks
期刊介绍: The primary aims of Computer Methods in Biomechanics and Biomedical Engineering are to provide a means of communicating the advances being made in the areas of biomechanics and biomedical engineering and to stimulate interest in the continually emerging computer based technologies which are being applied in these multidisciplinary subjects. Computer Methods in Biomechanics and Biomedical Engineering will also provide a focus for the importance of integrating the disciplines of engineering with medical technology and clinical expertise. Such integration will have a major impact on health care in the future.
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