Deep Learning-Based Abnormal Behavior Detection for Elderly Healthcare Using Consumer Network Cameras

Abnormal behavior has become the leading cause of injuries among the elderly in the modern society. Elderly anomaly is a widespread concern in both academic and industrial fields. Detection of abnormal behaviors using deep learning techniques has been pervasively investigated due to the flourishing of powerful edge computing and the decreasing costs of consumer electronics, such as commercial vision sensors. This paper intentionally designs an innovative architecture to detect the elderly typical abnormal behaviors: falls and tumbles, aggression and wandering, using the consumer network cameras, which are configured in the residential areas, healthcare centers, to name a few. Different from the conventional abnormal behavior detection methods, which suffer the issues of improper model generalization and lack of the spatial and temporal coherence, we design a deep learning model that robustly extracts the skeleton joints, detects and classifies the abnormal behaviors while considering the spatial and temporal context. It should be noted that the images are captured with the network cameras rigidly fixed at the typical elderly activity spots. The group of captured images will be fed to the local server, equipped with GPUs, for online monitoring and alarming. Our method is extensively evaluated on the elderly anomaly detection platform. It achieves the competitive performance with mAP greater than 85%.