Comparison of first order statistical and autoregressive model features for activity prediction

Abstract

Activity recognition is an important subject with many applications in health care, emergency care, and assisted living. Nowadays, activity information can be acquired using small accelerometers connected to the body, including the ones available in smartphones. In this study, we assessed the influence of autoregressive model parameters or features on activity detection or classification. Our results indicate that, compared to relatively simple features such as first order statistics, autoregressive model features have rather low impact in determining or improving performance of automatic activity detection using machine intelligence.