Amelioration of physical activity estimation from accelerometer sensors using prior knowledge

Human physical activity assessment using inertial sensor's data has become a prominent research area in the biomedical engineering field and an important application area for pattern recognition. This paper proposes to improve physical activity detection by combining prior knowledge concerning activity sequences with predictions of a support vector machine classifier (SVM). The temporal stable nature of activities is modeled by a directed graph Markov chain to reinforce decisions obtained using activity classes' confidence measures of a traditional SVM. We therefore review existing approaches dealing with determining these confidence measures for SVM classification. We then propose new methods for confidence measures estimation for SVM bi-class and multi-class problems. While applying the graph with proposed techniques for confidence estimation, results show superlative recognition rate of 92% for classifying 6 activities from data collected by a tri-axial accelerometer worn on belt.