Towards power optimized kalman filter for gait assessment using wearable sensors

Systems with wearable and wireless motion sensors have been receiving significant attention in the past few years specifically for the applications of human movement monitoring. One important concern in the design of wearable and wireless motion sensors, also referred to as Body Sensor Networks, is the form factor. A smaller form factor makes the device easily portable and wearable, hence improving users' acceptability. The form factor is usually determined by the size of the battery, which in turn is dependent on the power required by the system and the sensors present in it. Most human movement monitoring applications require inertial sensors like accelerometers and gyroscopes. However, the power consumption of a gyroscope is an order of magnitude greater than an accelerometer. In this paper, we examine power savings obtained by turning off the gyroscope for short periods while using Kalman filters to predict the state. The Kalman filter uses previous readings from both accelerometer and gyroscopes for its calculations. Our results show that with this approach, the system can achieve a reasonable reduction in power consumption with an acceptable loss of accuracy.