Video analytic system for activity profiling, fall detection, and unstable motion detection

Abstract

AbstractReal time detection of falls and unstable movement by elderly people is vital to their quality of life and safety. We present an edge processing device integrated with a cloud computation framework that can be used for activity profiling as well as trigger alerts for falls and unstable motion by elderly people at home. The proposed system uses fixed cameras to track and analyze each visible person in the scene, classifying their actions into nine ordinary activities, a fall, or unstable movement. An alert notification is sent to caregivers whenever a fall or unstable movement is detected. The major components of the system include an embedded device (NVIDIA JETSON TX2) and cloud-based storage and analysis infrastructure. The system is composed of modules for detecting, tracking and recognizing humans, a cascaded hierarchical classifier for nine ordinary activities and falls, and a long short-term memory (LSTM) module to predict unstable movement in video. The system is designed for accuracy, usability, and cost. A prototype system has been subjected to individual module tests along with a field test within a volunteer’s household. It achieved an accuracy of 91.6% for ordinary actions and falls with a recall of 97.02% for unstable motion. Future phases will expand deployment to multiple homes.