Automated Determination of Gait Parameters Using Depth Based Person Tracking

The purpose of this paper is the development of a method to extract gait parameters from an existing person tracker based on depth data. The underlying person tracking method integrates an algorithm for auto-calibration of the depth sensor, which allows plug-and-play installation. Based on the provided tracking information, gait velocity, distance, and duration is measured, as well as gait cycle components. The estimation of these components is based on a gait event demarcation approach using a wearable accelerometer, that provides similar signals as the person tracker. These signals are predicted by a random forest and post-processed using an optimization algorithm and a-priori information. To evaluate the system, we recorded a dataset with 10 healthy adults performing walks on 4 different paths. The detected gait cycle parts show deviations to the actual annotated components of 0.06-0.1s, and 0.69-9.42%. The velocity measurement achieves a mean error of 3.64cm/s, while the distance error results in 20.48cm, duration measurement obtains an average error of 0.27s.