Robot-Assisted and Wearable Sensor-Mediated Autonomous Gait Analysis§.

In this paper, we propose an autonomous gait analysis system consisting of a mobile robot and custom-engineered instrumented insoles. The robot is equipped with an on-board RGB-D sensor, the insoles feature inertial sensors and force sensitive resistors. This system is motivated by the need for a robot companion to engage older adults in walking exercises. Support vector regression (SVR) models were developed to extract accurate estimates of fundamental kinematic gait parameters (i.e., stride length, velocity, foot clearance, and step length), from data collected with the robot’s on-board RGB-D sensor and with the instrumented insoles during straight walking and turning tasks. The accuracy of each model was validated against ground-truth data measured by an optical motion capture system with N=10 subjects. Results suggest that the combined use of wearable and robot’s sensors yields more accurate gait estimates than either sub-system used independently. Additionally, SVR models are robust to inter-subject variability and type of walking task (i.e., straight walking vs. turning), thereby making it unnecessary to collect subject-specific or task-specific training data for the models. These findings indicate the potential of the synergistic use of autonomous mobile robots and wearable sensors for accurate out-of-the-lab gait analysis.