Personalized 1-Year Fall Risk Prediction Model for Older Adults Using Wearable Sensors
A dynamic nomogram-based clinical decision support tool integrating gait kinematics, frailty, and recent fall history
A personalized fall risk prediction model based on interpretable nomograms, developed to estimate the probability of falls within one year among older adults.
Key predictors were identified using multivariable logistic regression, integrating IMU-derived gait kinematic features, Clinical Frailty Scale (CFS), and recent fall history.
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