Device-Free Non-Privacy Invasive Indoor Human Posture Recognition Using Low-Resolution Infrared Sensor-Based Wireless Sensor Networks and DCNN

Human posture Recognition is the foundation of the human activity monitoring. The activity monitoring system is in high demand for the elderly living alone to monitor their health status and accidental fall since the world elderly population will be doubled by 2050. Researchers have developed many camera or wearable device-based human Recognition systems; however, they are considered to be privacy-invasive and/or not practical for the long-term monitoring. We propose a device-free unobtrusive indoor human posture Recognition system leveraging a low-resolution infrared sensor-based wireless sensor network and deep convolutional neural network (DCNN). We integrated AMG8833 sensor module with 8×8 thermal sensors for sensing the human body temperature and WiFi module for a wireless sensor network. Three wireless sensor nodes are used to capture 3-axis human thermal image. Totally, 15063 samples are collected from four volunteers while they had performed eight human postures as the ground truth for the 10-fold cross-validation of DCNN models. Experimental results indicate that the highest average F1-score for the eight postures was 0.9981. Thus, the proposed system has the high potential for monitoring elderly daily activities, exercise, and fall in emergency cases. Moreover, we believe that our proposed system will be a milestone in the device-free unobtrusive sensing technology.