College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China.
Sensors (Basel). 2021 May 20;21(10):3551. doi: 10.3390/s21103551.
Sensor-based human activity recognition (HAR) has attracted enormous interests due to its wide applications in the Internet of Things (IoT), smart homes and healthcare. In this paper, a low-resolution infrared array sensor-based HAR approach is proposed using the deep learning framework. The device-free sensing system leverages the infrared array sensor of 8×8 pixels to collect the infrared signals, which can ensure users' privacy and effectively reduce the deployment cost of the network. To reduce the influence of temperature variations, a combination of the J-filter noise reduction method and the Butterworth filter is performed to preprocess the infrared signals. Long short-term memory (LSTM), a representative recurrent neural network, is utilized to automatically extract characteristics from the infrared signal and build the recognition model. In addition, the real-time HAR interface is designed by embedding the LSTM model. Experimental results show that the typical daily activities can be classified with the recognition accuracy of 98.287%. The proposed approach yields a better result compared to the existing machine learning methods, and it provides a low-cost yet promising solution for privacy-preserving scenarios.
基于传感器的人体活动识别（HAR）由于在物联网（IoT）、智能家居和医疗保健中的广泛应用而引起了极大的关注。在本文中，提出了一种基于低分辨率红外阵列传感器的 HAR 方法，该方法使用深度学习框架。无设备感测系统利用 8x8 像素的红外阵列传感器来收集红外信号，这可以确保用户的隐私并有效地降低网络的部署成本。为了减少温度变化的影响，对红外信号进行了 J 滤波器降噪方法和巴特沃斯滤波器的组合预处理。长短期记忆（LSTM）是一种有代表性的递归神经网络，用于自动从红外信号中提取特征并构建识别模型。此外，通过嵌入 LSTM 模型设计了实时 HAR 接口。实验结果表明，典型的日常活动可以以 98.287%的识别准确率进行分类。与现有的机器学习方法相比，所提出的方法具有更好的结果，为隐私保护场景提供了一种低成本但很有前途的解决方案。
