Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers

TLDR

On-body wearable sensors are widely used in academic and industrial domains, especially for ambulatory monitoring and pervasive computing, where quantitative analysis of human motion and its automatic classification are key tasks. The paper aims to classify human physical activity using on-body accelerometers, focusing on computational algorithms, particularly Hidden Markov Models. The authors employ on-body accelerometers and Hidden Markov Models to classify activity, illustrated by analyzing a dataset of accelerometer time series. The example demonstrates the method’s application to accelerometer time series data.

Abstract

The use of on-body wearable sensors is widespread in several academic and industrial domains. Of great interest are their applications in ambulatory monitoring and pervasive computing systems; here, some quantitative analysis of human motion and its automatic classification are the main computational tasks to be pursued. In this paper, we discuss how human physical activity can be classified using on-body accelerometers, with a major emphasis devoted to the computational algorithms employed for this purpose. In particular, we motivate our current interest for classifiers based on Hidden Markov Models (HMMs). An example is illustrated and discussed by analysing a dataset of accelerometer time series.