Activity recognition using dynamic multiple sensor fusion in body sensor networks.
Multiple sensor fusion is a main research direction for activity recognition. However, there are two challenges in those systems: the energy consumption due to the wireless transmission and the classifier design because of the dynamic feature vector. This paper proposes a multi-sensor fusion framework, which consists of the sensor selection module and the hierarchical classifier. The sensor selection module adopts the convex optimization to select the sensor subset in real time. The hierarchical classifier combines the Decision Tree classifier with the Naïve Bayes classifier. The dataset collected from 8 subjects, who performed 8 scenario activities, was used to evaluate the proposed system. The results show that the proposed system can obviously reduce the energy consumption while guaranteeing the recognition accuracy.
Department of Electronic and Computer Engineering, Faculty of Science and Engineering, University of Limerick, Limerick, Ireland. lei.gao@ul.ie
Lei
L
Gao
Alan K
AK
Bourke
John
J
Nelson
D000203:Activities of Daily Living; D040421:Cell Phone; D001330:Electronic Data Processing; D002149:Energy Intake / Q000502:physiology*; D006801:Humans; D008954:Models, Biological; D012680:Sensitivity and Specificity