期刊
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
卷 25, 期 10, 页码 1812-1820出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNSRE.2017.2687100
关键词
Accelerometer; pressure-sensing insole; fall risk; older adults; artificial intelligence; machine learning; classifiers
资金
- Natural Sciences and Engineering Research Council of Canada
- Ontario Ministry of Training, Colleges and Universities
- University of Waterloo
Wearable sensors can provide quantitative, gait-based assessments that can translate to point-of-care environments. This investigation generated elderly fall-risk predictive models based on wearable-sensor-derived gait data and prospective fall occurrence, and identified the optimal sensor type, location, and combination for single and dual-task walking. 75 individuals who reported six month prospective fall occurrence (75.2 +/- 6.6 years; 47 non-fallers and 28 fallers) walked 7.62 m under single-task and dual-task conditions while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, and left and right shanks. Fall-risk classification models were assessed for all sensor combinations and three model types: neural network, naive Bayesian, and support vector machine. The best performing model used a neural network, dual-task gait data, and input parameters from head, pelvis, and left shank accelerometers (accuracy = 57%, sensitivity = 43%, and specificity = 65%). The best single-sensor model used a neural network, dual-task gait data, and pelvis accelerometer parameters (accuracy = 54%, sensitivity = 35%, and specificity = 67%). Single-task and dual-task gait assessments provided similar fall-risk model performance. Fall-risk predictivemodels developed for point-of-care environments should use multi-sensor dual-task gait assessment with the pelvis location considered if assessment is limited to a single sensor.
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