Digital signal processing (3rd ed.): principles, algorithms, and applications
Digital signal processing (3rd ed.): principles, algorithms, and applications
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Feature selection and activity recognition from wearable sensors
UCS'06 Proceedings of the Third international conference on Ubiquitous Computing Systems
A practical approach to recognizing physical activities
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
IEEE Transactions on Information Technology in Biomedicine
Activity Analysis, Summarization, and Visualization for Indoor Human Activity Monitoring
IEEE Transactions on Circuits and Systems for Video Technology
Technology and Health Care
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It is estimated that 10% of the patients admitted to North American hospitals die of hospital acquired infections. Approximately half of these are thought to be a consequence of poor hand hygiene practices by the hospital staff. Electronic hand washing reminders that prompt caregivers to wash their hands before and after the patient/patient's environment contact may help to increase the hand hygiene compliance rate. However, the current systems fail to identify the nursing procedures happening around the patient to issue proper hand hygiene prompt. In this research we used the hardware of a low-cost wireless Sony game controller, which included a 3-axis accelerometer, to identify six nursing activities happening around a patient. We attached five sensors to eight nurses' left and right wrists, left and right upper arms, and the backs. Each nurse performed 10 trials of each nursing activity in sequence, followed by a combined nursing activities trial. We extracted mean, standard deviation, energy, and correlation among axes per sensor and compared the results of 1-Nearest Neighbour (1-NN), Decision Tree (J48), and Naïve Bayes classifiers. 1-NN classifier had the best performance and on average regardless of the sensor locations, we achieved 84% ± 2% accuracy.