A rule-based approach to activity recognition
KICSS'10 Proceedings of the 5th international conference on Knowledge, information, and creativity support systems
Miniaturized wireless ECG monitor for real-time detection of epileptic seizures
ACM Transactions on Embedded Computing Systems (TECS) - Special Section on Wireless Health Systems, On-Chip and Off-Chip Network Architectures
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This paper describes an orientation-independent method for detecting activities of daily living based on reference coordinate transformation. With the proposed method, a classification model can be trained using data acquired during a specific sensor orientation and applied to other input signals regardless of the orientation of the device. The technique is validated using activity recognition experiments with four different orientations of a single tri-axial accelerometer placed on the waist of 13 subjects performing a sub-class of activities of daily living. A high subject-independent accuracy of 90.42% has been achieved, reflecting a significant improvement of 11.74% and 16.58%, compared with classification without input transformation and classification with orientation-specific models, respectively.