LAUSR

This paper presents an accurate, robust, wearable measurement system for foot clearance estimation along with algorithms to provide a real-time estimate of foot height and orientation. Different configurations of infrared distance meter sensors were used, both alone and in combination with an inertial measurement unit. In order to accurately estimate the foot clearance when in presence of daylight and when the foot orientation changes dynamically during walking, several algorithms were designed based on physics of sensors and tuned using the acquired data against a reference system. These algorithms, specific to the number of sensors, include the estimators of the foot orientation and estimators of the foot clearance. These estimators are tested on normal walking (rms error ≤ 8.4 mm) and walking with exaggerated step heights and inversion-eversion rotations. A Bayesian fusion of estimators was also implemented to better cope with the extreme and abnormal walking kinematics while maintaining a high performance for normal walking. All estimators were trained on uniformly distributed bootstrapped sub-samples of data and tested on several normal and abnormal walking data. The results proved the robustness of the proposed system against variations in the gait kinematics ( $\vert $ mean $\vert \pm $ standard deviation of error for heel and toe clearance was equal to or smaller than 3.1±9.3 mm when using a Bayesian fusion of three different estimators) and environment lighting (with an introduced error of 1 to 4% of actual distance).