Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Activity Recognition and Monitoring Using Multiple Sensors on Different Body Positions
BSN '06 Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks
Wearable Activity Tracking in Car Manufacturing
IEEE Pervasive Computing
From Backpacks to Smartphones: Past, Present, and Future of Wearable Computers
IEEE Pervasive Computing
Human daily activity recognition in robot-assisted living using multi-sensor fusion
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Performance metrics for activity recognition
ACM Transactions on Intelligent Systems and Technology (TIST)
A benchmark dataset to evaluate sensor displacement in activity recognition
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
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Ensuring ubiquity, robustness and continuity of monitoring is of key importance in activity recognition. To that end, multiple sensor configurations and fusion techniques are ever more used. In this paper we present a multi-sensor meta-classifier that aggregates the knowledge of several sensor-based decision entities to provide a unique and reliable activity classification. This model introduces a new weighting scheme which improves the rating of the impact that each entity has on the decision fusion process. Sensitivity and specificity are particularly considered as insertion and rejection weighting metrics instead of the overall accuracy classification performance proposed in a previous work. For the sake of comparison, both new and previous weighting models together with feature fusion models are tested on an extensive activity recognition benchmark dataset. The results demonstrate that the new weighting scheme enhances the decision aggregation thus leading to an improved recognition system.