Detection of subject’s intention to trigger transitions between sit, stand and walk with a lower limb exoskeleton

Abstract

In this study we explore a way of controlling a lower limb exoskeleton based on the detection of the user intention by recording and classifying information from force sensors placed on both knees and hips. The classifier is based on Linear Discriminant Analysis and has been tested offline in 5 healthy subjects, obtaining an average accuracy of 91.11 % for the sit-to-stand transition, 72.5 % for the stand-to-walk transition and 70 % for the stand-to-sit transition.