Efficient health-related abnormal behavior detection with visual and inertial sensor integration

An increasing number of healthcare issues arise from unsafe abnormal behaviors such as falling and staggering of a rapidly aging population. These abnormal behaviors, often coming with abrupt movements, could potentially be life-threatening if unnoticed; real-time, accurate detection of this sort of behavior is essential for timely response. However, it is challenging to achieve generic, while accurate, abnormal behavior detection in real time with moderate sensing devices and processing power. This paper presents an innovative system as a solution. It utilizes primarily visual data for detecting various types of abnormal behaviors due to accuracy and generality of computer vision technologies. Unfortunately, the volume of the recorded video data is huge, which is preventive to process all in real time. We propose to use elder-carried mobile devices either by a dedicated design or by a smartphone, equipped with inertial sensor to trigger the selection of relevant video data. In this way, the system operates in a trigger verify fashion, which leads to selective utilization of video data to guarantee both accuracy and efficiency in detection. The system is designed and implemented using inexpensive commercial off-the-shelf sensors and smartphones. Experimental evaluations in real-world settings illustrate our system’s promise for real-time accurate detection of abnormal behaviors.