Vision-Assisted Human Motion Analysis for Bed Exit Prediction Model Construction

Abstract

This paper presents a vision-assisted human motion analysis method by which to classify unknown preparatory motions for bed exit. A convolutional neural network was applied to identify the existence of essential postures between two meaningful sequential motions from low-resolution depth images. The preliminary experimental results showed that sequential images of bed exit preparatory motions can be classified as essential motion states, which is useful for construction of the states of a hidden Markov model for bed exit prediction.