Assessment of human locomotion by using an insole measurement system and artificial neural networks.

Abstract

A new method for measuring and characterizing free-living human locomotion is presented. A portable device was developed to objectively record and measure foot-ground contact information in every step for up to 24h. An artificial neural network (ANN) was developed to identify the type and intensity of locomotion. Forty subjects participated in the study. The subjects performed level walking, running, ascending and descending stairs at slow, normal and fast speeds determined by each subject, respectively. The device correctly identified walking, running, ascending and descending stairs (accuracy 98.78%, 98.33%, 97.33%, and 97.29% respectively) among different types of activities. It was also able to determine the speed of walking and running. The correlation between actual speed and estimated speed is 0.98, p< 0.0001. The average error of walking and running speed estimation is -0.050+/-0.747 km/h (mean +/- standard deviation). The study has shown the measurement of duration, frequency, type, and intensity of locomotion highly accurate using the new device and an ANN. It provides an alternative tool to the use of a gait lab to quantitatively study locomotion with high accuracy via a small, light and portable device, and to do so under free-living conditions for the clinical applications.