Abstract
Wearable sensors have potential for quantitative, gait-based, point-of-care fall risk assessment that can be easily and quickly implemented in clinical-care and older-adult living environments. This investigation generated models for wearable-sensor based fall-risk classification in older adults and identified the optimal sensor type, location, combination, and modelling method; for walking with and without a cognitive load task. A convenience sample of 100 older individuals (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) 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. Participants also completed the Activities-specific Balance Confidence scale, Community Health Activities Model Program for Seniors questionnaire, six minute walk test, and ranked their fear of falling. Fall risk classification models were assessed for all sensor combinations and three model types: multi-layer perceptron neural network, naïve Bayesian, and support vector machine. The best performing model was a multi-layer perceptron neural network with input parameters from pressure-sensing insoles and head, pelvis, and left shank accelerometers (accuracy = 84%, F1 score = 0.600, MCC score = 0.521). Head sensor-based models had the best performance of the single-sensor models for single-task gait assessment. Single-task gait assessment models outperformed models based on dual-task walking or clinical assessment data. Support vector machines and neural networks were the best modelling technique for fall risk classification. Fall risk classification models developed for point-of-care environments should be developed using support vector machines and neural networks, with a multi-sensor single-task gait assessment.
Highlights
Falls are a serious health concern for the elderly, with 30% of individuals older than 65 years falling each year [1], costing approximately 20 billion dollars a year in the United States [2]
This paper presents a comprehensive investigation of fall-risk classification capabilities that included two types of wearable sensors, four accelerometer locations, and three types of models
Of the best 50 fall-risk classifier models based on ST data (Table 3), the top four models (I-P support vector machine (SVM), I-H-P SVM, I-P neural network (NN), I-H-P-LS NN) had identical top ranking scores with an accuracy of 84%, F1 0.600, and Matthew’s Correlation Coefficient (MCC) 0.521
Summary
Falls are a serious health concern for the elderly, with 30% of individuals older than 65 years falling each year [1], costing approximately 20 billion dollars a year in the United States [2]. Half of these falls occur during walking activities [3]. Wearable sensors have potential for quantitative, gait-based, point-of-care fall risk assessment that can be and quickly implemented in clinical-care and older-adult living environments. A wide variety of wearable-sensor, inertial-based variables have been used to predict and classify fall risk with varying levels of success (accuracy: 62–100%, specificity: 35–100%, sensitivity: 55– 99%) [6]. Non-health care applications include turbulence [12], flow [13,14], and chaotic [15] dynamics analysis
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