An automatic segmentation technique in body sensor networks based on signal energy

Monitoring human activities using wearable wireless sensor nodes has the potential to enable many useful applications for everyday situations. The long-term lifestyle monitoring can greatly improve healthcare by gathering information about quality of life; aiding the diagnosis and tracking of certain diseases such as Parkinson's. The deployment of an automatic and computationally-efficient algorithm reduces the complexities involved in the detection and recognition of human activities in a distributed system. This paper presents a new algorithm for automatic segmentation of routine human activities. The proposed algorithm can distinguish between discrete periods of activity and rest without specifically knowing the activity. A finite subset of nodes can detect all human activities, but each node by itself can only detect a particular set of activities. For local segmentation we choose the parameters for each node that result in the least segmentation error. We demonstrate the effectiveness of our algorithm on data collected from body sensor networks for a scenario simulating a set of daily activities.