Human Activity Recognition by machine learning methods

Abstract

Recognition of human activity from sensor data is a research field of great potential. Giving autonomous systems the ability to identify what a human subject is doing at a given time is highly useful in many industries, particularly in health care and security monitoring. Our results, using a public domain dataset, show that the state- of-the-art decision tree ensemble algorithm XGBoost gives an accuracy of 94.6% validated on an independent test set. Previously published results using support vector machines (SVM) gave an accuracy of 90.2%. As far as we know, our result is the new state of the art for this data set. Recognition of human activity carries potential privacy concerns, which to some degree constrain the choice of sensor technology. Therefore, systems such as ours which can identify activities from simple inertial sensors, e.g. accelerators and gyroscopes are of particular interest. Data from such inertial sensors are difficult to interpret using mechanistic models; hence the field of Machine Learning is particularly interesting for this application.