IMU based single stride identification of humans

To facilitate human-robot interactions with the user, it is necessary for the robot to identify the interaction partner. We propose the use of a single wearable sensor worn at the center of the user's belt to record the gait when the interaction partner approaches the robot. Based on the data of a single gait cycle recorded with a single inertial measurement unit (IMU), we identify a person by his/her walking style. For identification, we first detect individual strides. We introduce a simple feature that characterizes the individual's asymmetry of gait and classify the individual using a Bayes classifier. To evaluate our approach, we collect motion data from 20 persons; the classification accuracy based on the proposed asymmetry-based feature reaches 99.3%. We further investigate the robustness of our approach against slight variations in the sensor placement, variations in speed, and walking straight versus walking on a curved route.