Seamless Vision-assisted Placement Calibration for Wearable Inertial Sensors

Abstract

Wearable inertial devices are being widely used in the applications of activity tracking, health care, and professional sports, and their usage is on a rapid rise. Signal processing algorithms for these devices are often designed to work with a known location of the wearable sensor on the body. However, in reality, the wearable sensor may be worn at different body locations due to the user's preference or unintentional misplacement. The calibration of the sensor location is important to ensure that the algorithms operate correctly. In this article, we propose an auto-calibration technique for determining the location of wearables on the body by fusing the 3-axis accelerometer data from the devices and three-dimensional camera (i.e., Kinect) information obtained from the environment. The automatic calibration is achieved by a cascade decision-tree-based classifier on top of the minimum least-squares errors obtained by solving Wahba's problem, operating on heterogeneous sensors. The core contribution of our work is that there is no extra burden on the user as a result of this technique. The calibration is done seamlessly, leveraging sensor fusion in an Internet-of-Things setting opportunistically when the user is present in front of an environmental camera performing arbitrary movements. Our approach is evaluated with two different types of movements: simple actions (e.g., sit-to-stand or picking up phone) and complicated tasks (e.g., cooking or playing basketball), yielding 100% and 82.56% recall for simple actions and for complicated tasks, respectively, in determining the correct location of sensors.