Automatic detection of the onset of nursing activities using accelerometers and adaptive segmentation.
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.
Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Canada. kaveh.momen@utoronto.ca
Kaveh
K
Momen
Geoff R
GR
Fernie
D056044:Actigraphy / Q000295:instrumentation*; D000465:Algorithms; D003625:Data Collection / Q000379:methods; D006235:Hand Disinfection; D006801:Humans; D017053:Infection Control; D009732:Nursing Care; D009864:Ontario