Multi-sensor data fusion within the belief functions framework: application to smart home services
NEW2AN'11/ruSMART'11 Proceedings of the 11th international conference and 4th international conference on Smart spaces and next generation wired/wireless networking
Weight factor algorithms for activity recognition in lattice-based sensor fusion
KSEM'11 Proceedings of the 5th international conference on Knowledge Science, Engineering and Management
Context awareness for a smart environment utilizing context maps and dempster-shafer theory
ICOST'12 Proceedings of the 10th international smart homes and health telematics conference on Impact Ananlysis of Solutions for Chronic Disease Prevention and Management
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This paper explores an improvement to activity recognition within a Smart Home environment using the Dempster-Shafer theory of evidence. This approach has the ability to be used to monitor human activities in addition to managing uncertainty in sensor based readings. A three layer lattice structure has been proposed, which can be used to combine the mass functions derived from sensors along with sensor context and subsequently can be used to infer activities. From the total 209 recorded activities throughout a two week period [9], 85 toileting activities were considered. The results from this work demonstrated that this method was capable of detecting 75 of the toileting activities correctly within a Smart Home environment equating to a classification accuracy of 88.2%.