Supervised and unsupervised classification approaches for human activity recognition using body-mounted sensors

In this paper, the activity recognition problem from 3-d acceleration data measured with body-worn accelerometers is formulated as a problem of multidimensional time series segmentation and classification. More specifically, the proposed approach uses a statistical model based on Multiple Hidden Markov Model Regression (MHMMR) to automatically analyze the human activity. The method takes into account the sequential appearance and temporal evolution of the data to easily detect activities and transitions. Classification results obtained by the proposed approach and compared to those of the standard supervised classification approaches as well as the standard hidden Markov model show that the proposed approach is promising.