A sparse representation of physical activity video in the study of obesity

Wearable visual devices have many emerging applications in human health monitoring, such as the study of food intake and physical activities. However, these devices produce large amounts of data which must be processed efficiently and effectively. In this paper, we present a video- based health monitoring system for physical activity studies. We utilize an efficient signal extraction method to process and reduce the effects of noises on field-acquired image data. We further develop a maximum likelihood estimator to estimate the walking speed and classify activity patterns (walking or running) in real-time under both indoor and outdoor environments. We demonstrate the feasibility of the proposed method using three different test video sequences.