Journal Article

Device-Free Human Activity Recognition with Low-Resolution Infrared Array Sensor Using Long Short-Term Memory Neural Network

Chen, Deying, Miao, Xiren, Yin, Cunyi, Jiang, Hao, Chen, Jing

2021

Overview

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.

• Publisher: MDPI AG,MDPI
• Subject: Artificial intelligence / Cameras / Costs / Deep learning / Human Activities / human activity recognition (HAR) / Humans / Internet of Things / long short-term memory (LSTM) / low-resolution infrared array sensor / Machine Learning / Neural networks / Neural Networks, Computer / Noise / Privacy / Sensors / Time series / Wearable computers / Wireless networks