An algorithm for real time minimum toe clearance estimation from signal of in-shoe motion sensor.

Abstract

An algorithm has been constructed for estimating minimum toe clearance (MTC), an important gait parameter previously proven to be a critical indicator of tripping risk. It uses data from a previously reported in-shoe motion sensor (IMS) for detecting gait events. First, candidate feature points in the IMS signal for use in detecting MTC events were identified. Then, the temporal agreement between each feature point and target MTC event was evaluated. Next, the accuracy and precision of the MTC estimated using each feature point was evaluated using a reference value obtained using a 3-D optical motion-capture system. The MTC was estimated using a geometric model and the IMS signal corresponding to the predicted MTC event. Once the best candidate feature point was identified, a real-time MTC estimation algorithm for use with an IMS was constructed. The mean values and standard deviations of measured foot motions obtained in a previous study were used for evaluating accuracy and precision. The results suggest that MTC events can be estimated by detecting the crossing point between the acceleration waveforms in the anterior-posterior and superior-inferior directions in an accuracy of 2.0% gait cycle. Using this feature point enables the MTC to be estimated in real time with an accuracy of 8.6 mm, which will enable monitoring of MTC in daily living.