An ontological approach for context-aware reminders in assisted living' behavior simulation
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Proceedings of the 2013 international conference on Intelligent user interfaces
Activity logging using lightweight classification techniques in mobile devices
Personal and Ubiquitous Computing
Self-management of COPD: a technology driven paradigm
Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication
My act: an automatic daily caloric estimation based on physical activity data using smart phones
Proceedings of the 7th International Convention on Rehabilitation Engineering and Assistive Technology
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This paper presents details of a convenient and unobtrusive system for monitoring daily activities. A smart phone equipped with an embedded 3D-accelerometer was worn on the belt for the purposes of data recording. Once collected the data was processed to identify 6 activities offline (walking, posture transition, gentle motion, standing, sitting and lying). The processing technique adopted a novel hierarchical classification. In the first instance, rule-based reasoning is used to discriminate between motion and motionless activities. Following this the classification process utilizes two multiclass SVM (support vector machines) classifiers to classify the motion and motionless activities, respectively. The classifiers were trained on data from one subject and tested on 10 subjects. The experiments demonstrate that the hierarchical method can reduce misclassification between motion and motionless activities. The average accuracy was improved compared with using a single classifier by using this classification method (82.8% vs. 63.8%), and is important for providing appropriate feedback in free living applications.