Predicting Seat-Off and Detecting Start-of-Assistance Events for Assisting Sit-to-Stand With an Exoskeleton

Abstract

Accurate and reliable event prediction is imperative for supporting movement with an exoskeleton. Two events are important during a sit-to-stand movement: seat-off, the event at which the subject leaves the chair and start-of-assistance for hip and knee, the earliest time at which assistance may be provided. This letter analyzes two methods to predict and detect these events. Both methods only have joint encoder data as input. The model-based method uses probabilistic principle component analysis with a Kalman filter. Based on a statistically learned model, a joint trajectory is predicted. The seat-off event is predicted using its correlation with maximum hip angle. Since the start-of-assistance event has no clear correlation with joint trajectories, it cannot be detected with this method. The model-free method is a feed-forward neural network, which learns a mapping between inputs and events directly. It is applied to both seat-off prediction and start-of-assistance detection. Methods have been evaluated on 311 lab-recorded movements. For the seat-off event, the model-based method is more reliable than the model-free method. For the start-of-assistance event, the model-free method performs well, except in an outlier case for one subject. Both of these methods allow accurate and reliable event prediction, only using joint encoder data as inputs.