Classification of Human Physical Activities from On-body Accelerometers - A Markov Modeling Approach.

Several applications demanding the development of small networks of on-body sensors, such as motion sensors, are currently investigated. Accelerometers are a popular choice as motion sensors: the reason is partly in their capability of extracting information that can be used to automatically infer the physical activity the human subject is involved, beside their role in feeding estimators of biomechanical parameters. Automatic classification of human physical activities is highly attractive for pervasive computing systems, whereas contextual awareness may ease the human-machine interaction, and in biomedicine, whereas wearable sensor systems are proposed for long-term monitoring of physiological and biomechanical parameters. This paper is concerned with the machine learning algorithms needed to perform the classification task. Hidden Markov Model (HMM) classifiers are studied by contrasting them with Gaussian Mixture Model (GMM) classifiers. HMMs incorporate the statistical information available on movement dynamics into the classification process, without discarding the time history of previous outcomes, as GMMs do. In this work, rather than considering them as models for single motor activities, we apply HMMs as models suitable for sequences of chained activities. An example of the benefits of the statistical leverage by HMMs is illustrated and discussed by analyzing a dataset of accelerometer time series.