Simulator Systems Section, Aeronautical System Research Division, National Chung-Shan Institute of Science and Technology, Taichung 407, Taiwan.
Department of Electrical Engineering, National Chung Hsing University, Taichung 402, Taiwan.
Sensors (Basel). 2022 Nov 4;22(21):8507. doi: 10.3390/s22218507.
Human activity recognition (HAR) became a challenging issue in recent years. In this paper, we propose a novel approach to tackle indistinguishable activity recognition based on human wearable sensors. Generally speaking, vision-based solutions struggle with low illumination environments and partial occlusion problems. In contrast, wearable inertial sensors can tackle this problem and avoid revealing personal privacy. We address the issue by building a multistage deep neural network framework that interprets accelerometer, gyroscope, and magnetometer data that provide useful information of human activities. Initially, the stage of variational autoencoders (VAE) can extract the crucial information from raw data of inertial measurement units (IMUs). Furthermore, the stage of generative adversarial networks (GANs) can generate more realistic human activities. Finally, the transfer learning method is applied to enhance the performance of the target domain, which builds a robust and effective model to recognize human activities.
人体活动识别（HAR）近年来成为一个具有挑战性的问题。在本文中，我们提出了一种基于人体可穿戴传感器的新颖方法来解决难以区分的活动识别问题。一般来说，基于视觉的解决方案在低光照环境和部分遮挡问题方面存在困难。相比之下，可穿戴惯性传感器可以解决这个问题，同时避免泄露个人隐私。我们通过构建一个多阶段深度神经网络框架来解决这个问题，该框架可以解释加速度计、陀螺仪和磁力计数据，从而提供有关人体活动的有用信息。最初，变分自动编码器（VAE）的阶段可以从惯性测量单元（IMU）的原始数据中提取关键信息。此外，生成对抗网络（GAN）的阶段可以生成更逼真的人体活动。最后，应用迁移学习方法来提高目标域的性能，从而构建一个强大而有效的模型来识别人体活动。
