Human Activity Recognition of Exoskeleton Robot with Supervised Learning Techniques

Abstract Lower limbs exoskeleton robots improve the motor ability of humans and can facilitate superior rehabilitative training. By training large datasets, many of the currently available mobile and signal devices that may be worn on the body can employ machine learning approaches to forecast and classify people's movement characteristics. This approach could help exoskeleton robots improve their ability to predict human activities. Two popular data sets are PAMAP2, which was obtained by measuring people's movement through inertial sensors, and WISDM, which was collected people's activity information through mobile phones. With the focus on human activity recognition, this paper applied the traditional machine learning method and deep learning method to train and test these datasets, whereby it was found that the prediction performance of a decision tree model was highest on these two data sets, which is 99% and 72% separately, and the time consumption of decision tree is the least. In addition, a comparison of the signals collected from different parts of the human body showed that the signals deriving from the hands presented the best performance in terms of recognizing human movement types.