Processing body sensor data streams for continuous physiological monitoring

With the recent advancement in the wearable sensor technology, various body sensor network systems are being incorporated in the garments to monitor continuous physiological as well as motor behavior of an individual. The raw physiological time series data coming from on-body sensors requires a thorough analysis for extraction of meaningful information. In addition, extracted information need to be presented/recommended to monitoring personnel/self to derive the high-level interpretation of the physiological state without having domain knowledge. In this paper, we propose a knowledge management system that extracts and conveys the information of the physiological states using individualized factor analysis model. The factor analysis based on the quantitative features extracted from the raw data streams provides the hidden knowledge components in the form of latent factors. We tested this system on the raw electromyogram signals from the hand muscles collected during the continuous monitoring of repetitive hand movements, where the hidden information in the form of intensity level of the activity and the muscle fatigue was extracted from the time and frequency domain features.