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 framewo...

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Bibliographic Details
Main Authors: Lei Gao, Bourke, A. K., Nelson, J.
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Decision trees
Energy consumption
Heuristic algorithms
Sensor fusion
Vectors
Wireless communication
Online Access:Request full text
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Summary: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.
ISSN:1094-687X
1558-4615
2694-0604
DOI:10.1109/EMBC.2012.6346121