Activity recognition using body mounted sensors: An unsupervised learning based approach

Unsupervised learning approaches are used in various applications such as speech recognition, image compression, information retrieval and activity recognition. This paper introduces a novel unsupervised approach for clustering multi-dimensional time series that present the 3-d acceleration data measured with body-worn accelerometers. More specifically, the proposed approach uses a statistical model based on Multiple Hidden Markov Model Regression (MHMMR) to automatically analyze the human activity. This method takes into account the sequential appearance and temporal evolution of the data to easily detect static and dynamic activities. Comparisons with existing unsupervised approaches, including the standard Gaussian Mixture Model, the k-means algorithm, the DBSCAN algorithm and the standard HMM, demonstrate the effectiveness of the proposed approach.