Abstract
In-bed motion detection is an important technique that can enable an array of applications, among which are sleep monitoring and abnormal movement detection. In this paper, we present a low-cost, low-overhead, and highly robust system for in-bed movement detection and classification that uses low-end load cells. By observing the forces sensed by the load cells, placed under each bed leg, we can detect many different types of movements, and further classify them as big or small depending on magnitude of the force changes on the load cells. We have designed three different features, which we refer to as Log-Peak, Energy-Peak, ZeroX-Valley, that can effectively extract body movement signals from load cell data that are collected through wireless links in an energy-efficient manner. After establishing the feature values, we employ a simple threshold-based algorithm to detect and classify movements. We have conducted thorough evaluation, that involves collecting data from 30 subjects who perform 27 pre-defined movements in an experiment. By comparing our detection and classification results against the ground truth captured by a video camera, we show the Log-Peak strategy can detect these 27 types of movements at an error rate of 6.3% while classifying them to big or small movements at an error rate of 4.2%.