Deploying Sensors for Gravity Measurement in a Body-Area Inertial Sensor Network

This paper deals with human posture tracking by deploying accelerometers on a human body. One fundamental issue in such scenarios is how to calculate the gravity. This is very challenging especially when the human body parts keep on moving. Fortunately, it is likely that there is a point of the body that touches the ground in most cases. This allows sensors to collaboratively calculate the gravity vector. Assuming multiple accelerometers being deployed on a rigid part of a human body, a recent work proposes a data fusion method to estimate the gravity vector on that rigid part. However, finding the optimal deployment of sensors that minimizes the estimation error of the gravity vector is not addressed. In this paper, we formulate the deployment optimization problem and propose two heuristics, called Metropolis-based method and largest-inter-distance-based method. Simulation and real experimental results show that our schemes are quite effective in finding near-optimal solutions for a variety of rigid body geometries.