Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
A Method for Clustering the Experiences of a Mobile Robot that Accords with Human Judgments
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Probabilistic discovery of time series motifs
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
An analysis of a large scale habitat monitoring application
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Demonstration of Active Guidance with SmartCane
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
SmartFall: an automatic fall detection system based on subsequence matching for the SmartCane
BodyNets '09 Proceedings of the Fourth International Conference on Body Area Networks
Toward unsupervised activity discovery using multi-dimensional motif detection in time series
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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We explore statistical regression techniques for use in medical monitoring and telehealth applications. Medical embedded systems of the present and future are recording vast sets of data related to medical conditions and physiology. In this paper, distributed time-lag linear models are proposed as a means to help explain relationships between two or more medical and physiological measurements. The issues associated with performing multiple regression with heterogeneous medical data are treated as problems in model selection. An automatic method of model selection is proposed to construct models for high sample rate data by grouping sets of predictor variables. The grouped predictor variable model optimization problem is formalized. Once an initial regression is performed on all available variables, our approximate algorithm for finding the grouped variable model with the greatest validity runs in O(n2) time, where n is the number of available predictor variables. This is compared to the all subsets technique which requires O(2n) time for the same predictor set. In our experiments with medical signal data, we find that the method produces models with reasonable goodness of fit scores and high average confidence levels for grouped predictors.