title: Prospective Fall-Risk Prediction Models for Older Adults Based on Wearable Sensors.
abstract: 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-sensinginsoles and tri-axial accelerometers at the head, pelvis, and left and right shanks. Fall-risk classificationmodels were assessed for all sensor combinations and three model types: neural network, naïve 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 predictive models 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.
authors.forename: Jennifer
authors.initials: J
authors.lastname: Howcroft
authors.forename: Jonathan
authors.initials: J
authors.lastname: Kofman
authors.forename: Edward D
authors.initials: ED
authors.lastname: Lemaire
mesh_terms: D061725:Accelerometry / Q000295:instrumentation*; D000058:Accidental Falls / Q000706:statistics & numerical data*; D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D001696:Biomechanical Phenomena; D005260:Female; D005544:Forecasting; D006257:Head; D006801:Humans; D007866:Leg; D000069550:Machine Learning; D008297:Male; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010388:Pelvis; D011446:Prospective Studies; D018570:Risk Assessment; D012775:Shoes; D060388:Support Vector Machine; D000076251:Wearable Electronic Devices