Prediction of knee angle from accelerometer data for microcontroller implementation of semi-active knee prosthesis

Abstract

In this study, the gait phase determination from accelerometer data is discussed for semi-active leg prosthesis for microcontroller implementation. The gait phase prediction is aimed by using knee angle obtained from the image of walking subject and accelerometer data recorded synchronously in the laboratory. For the phase determination of a gait, an artificial neural network is used because of its adaptive features for variable path and user. The accelerometer and knee angle data are prepared for the training and the testing set of the artificial neural network. The applicable network structure to be used in microcontroller based artificial knee is investigated and their performances are tested in terms of the the number of neurons and data window size.