Intention detection during gait initiation using supervised learning

Abstract

This paper presents a study of gait intention detection using force plates, inertial measurement units and an optical measurement system. The main goal is to detect gait initiation before heel-off and toe-off. Several established supervised machine learning methods are used to detect the onset of gait initiation, the first heel-off and the first toe-off. Events manually annotated by an expert serve as a reference. Results show that force plate signals are the most useful sensor, allowing gait onset to be detected with a mean absolute error of 0.12 seconds. Inertial measurement units are less accurate, with a mean absolute error for gait onset detection of 0.22 seconds. However, the decreased accuracy is primarily due to a small number of poorly detected outliers. The accuracy of the different supervised methods is also compared. For practical use, we recommend a combination of inertial measurement units and in-shoe pressure sensors, with different supervised methods used to detect different events.