Gait segmentation using bipedal foot pressure patterns

We present an automated gait segmentation method based on the analysis of foot plantar pressure patterns elaborated from two wireless pressure-sensitive insoles. The 64 pressure signals recorded by each device are elaborated to extract 10 feature variables which are used to segment the gait cycle into 6 sub-phases following a simplified version of Perry's gait model. The method is based on a Hidden Markov Model with a minimum phase length constraint and a univariate Gaussian emission model, which is decoded using a classic Viterbi algorithm. The method is tested on a pool of 5 healthy young subjects walking at two different speeds, through a leave-one-out cross-subject validation. The results show that the method is highly effective, yielding to an average performance of about 95% of correct phase classification, and 85 to 90% of phase transitions detected inside an acceptance window of 50ms.