Abstract
Fall-risk classification is a challenging but necessary task to enable the recommendation of preventative programs for individuals identified at risk for falling. Existing research has primarily focused on older adults, with no predictive fall-risk models for lower limb amputees, despite their greater likelihood of fall-risk than older adults. In this study, 89 amputees with varying degrees of lower limb amputation were asked if they had fallen in the past 6 months. Those who reported at least one fall were considered a fall risk. Each participant performed a 6 minute walk test (6MWT) with an Android smartphone placed in a holder located on the back of the pelvis. A fall-risk classification method was developed using data from sensors within the smartphone. The Ottawa Hospital Rehabilitation Center Walk Test app captured accelerometer and gyroscope data during the 6MWT. From this data, foot strikes were identified, and 248 features were extracted from the collection of steps. Steps were segmented into turn and straight walking, and four different data sets were created: turn steps, straightaway steps, straightaway and turn steps, and all steps. From these, three feature selection techniques (correlation-based feature selection, relief F, and extra trees classifier ensemble) were used to eliminate redundant or ineffective features. Each feature subset was tested with a random forest classifier and optimized for the best number of trees. The best model used turn data, with three features selected by Correlation-based feature selection (CFS), and used 500 trees in a random forest classifier. The resulting metrics were 81.3% accuracy, 57.2% sensitivity, 94.9% specificity, a Matthews correlation coefficient of 0.587, and an F1 score of 0.83. Since the outcomes are comparable to metrics achieved by existing clinical tests, the classifier may be viable for use in clinical practice.
Highlights
Falling is the second leading cause of accidental injury death [1]
The best model used turn data, with three features selected by Correlation-based feature selection (CFS), and used 500 trees in a random forest classifier
Since the outcomes are comparable to metrics achieved by existing clinical tests, the classifier may be viable for use in clinical practice
Summary
Falling is the second leading cause of accidental injury death [1]. Injuries related to falling can be debilitating, life-altering, and lead to lower self-perceived balance confidence [2]. While more than 26 fall risk assessment tools are available for clinicians [4], evolving wearable sensors systems can provide opportunities to augment common clinical mobility tests for fall risk identification. Wearable technology has been used to develop fall-risk classifiers that provide accurate and automated fall risk classification, to enable timely intervention with fall-risk mitigation techniques. Research reports differences in turning strategies between fall-risk and no fall-risk populations, and that viable elderly fall-risk classification can be achieved with turn data [5,9]. If fall risk classification can be determined from the 6MWT, clinic time could be more efficiently used by reducing the number of tests in a session
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