A Simple 2D Wheelchair Positioning: Correcting IMU’s Data Using One Encoder and Applying Kalman Filtering

Abstract

Inertial Measurement Units (IMU) are widely used for spatial positioning. They are well known, however, for signal drift. A common way of overcoming the drift is to use Kalman Filtering. In this study, we have undertaken some experiments during wheelchair propulsion, recording data with an IMU, an Encoder (tachometer) and an Optotrak (motion analysis system). We then applied Kalman filtering (with two approaches) to IMU’s data. Eventually, in order to verify Kalman’s results, they were compared to Optotrak’s data. As result of this study, 2D wheelchair tracking can be done with acceptable precision, using one IMU and one Encoder and applying Kalman filtering. Kalman filtering with approach B was a better predictor of subject’s spatial position than approach A. Kalman and even IMU’s results for rotation were of good accuracy; therefore IMU’s data can be used to find all angular characteristics of subject’s position, even without applying Kalman filtering, if the offsets are precisely found through a stationary test.