C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Machine Learning
Telos: enabling ultra-low power wireless research
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Activity Recognition for Everyday Life on Mobile Phones
UAHCI '09 Proceedings of the 5th International on ConferenceUniversal Access in Human-Computer Interaction. Part II: Intelligent and Ubiquitous Interaction Environments
Wireless sensor networks for personal health monitoring: Issues and an implementation
Computer Communications
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
IEEE Transactions on Information Technology in Biomedicine - Special section on new and emerging technologies in bioinformatics and bioengineering
IEEE Transactions on Information Technology in Biomedicine
Centinela: A human activity recognition system based on acceleration and vital sign data
Pervasive and Mobile Computing
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This paper presents a wireless body area network platform that performs physical activities recognition using accelerometers, biosignals and smartphones. Multiple classifiers and sensor combinations were examined to identify the classifier with the best recognition performance for the static and dynamic activities. The Functional Trees classifier proved to provide the best results among the classifiers evaluated Naive Bayes, Bayesian Networks, Support Vector Machines and Decision Trees [C4.5, Random Forest] and was used to train the model which was implemented for the real time activity recognition on the smartphone. The identified patterns of daily physical activities were used to examine conformance with medical advice, regarding physical activity guidelines. An algorithm based on Skip Chain Conditional Random Fields, received as inputs the recognized activities and data retrieved from the GPS receiver of the smartphone to develop dynamic daily patterns that enhance prediction results. The presented platform can be extended to be used in the prevention of short-term complications of metabolic diseases such as diabetes.