Computerized analysis of daily life motor activity for ambulatory monitoring
Technology and Health Care
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Identifying Activities of Daily Living Using Wireless Kinematic Sensors and Data Mining Algorithms
BSN '09 Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks
Activity recognition from accelerometer data
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
Technology and Health Care
Mining, knowledge and decision support
Technology and Health Care
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
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We used the Recursive Least Squares algorithm and a predictor filter to automatically identify the start and stop times of 6 simple nursing activities. The dataset included continuous acceleration recordings obtained with a single accelerometer sensor attached to the backs of 8 nurses. The algorithm requires no training. It identifies the start and stop time of each activity when at least 2 of 3 axes show significant acceleration changes not more than a second apart. The overall accuracy of the algorithm for a total of 96 start and stop events was 86.46% ± 12.55%. The accuracy was higher than 91% for 5 out of 8 subjects. The algorithm also indicated the onset of subcomponents of nursing activities for the majority of the subjects. The results of this study suggest that the presented algorithm may be useful in identifying transition points of human activities recorded with accelerometers.