Algorithm for temporal gait analysis using wireless foot-mounted accelerometers

Abstract

We present a new signal processing algorithm that extracts five gait events: heel strike, toe strike, heel-off, toe-off, and heel clearance from only two accelerometers attached on the heels of the subjects usual shoes. This algorithm first uses a continuous wavelet-based segmentation that parses the signal of consecutive strides into motionless periods defining relevant local acceleration signals. Then, the algorithm uses versatile techniques to accurately extract the five gait events from these local acceleration signals. We validated, on a stride-by-stride basis, the extraction of these gait events by comparing the results with reference data provided by a kinematic 3D analysis system and a video camera. The accuracy and precision achieved by the extraction algorithm for healthy subjects, the reduced number of accelerometer units required, and the validation results obtained, encourage us to further study this system in pathological conditions.