Identification of the cause of fall during the pre-impact fall period

Abstract

This study aimed to develop and validate a method for identifying factors that may cause a fall during the pre-impact fall period using wearable sensors. [Participants and Methods] The participants were 23 young people from the public data set (mean age, 23.4 years). Acceleration and angular velocity information obtained from sensors attached to the participant's waist was used to generate the pre-impact fall. The cause of the fall (slip, trip, fainting, get up, sit down) was then classified with and without the addition of activity of daily living data using three different support vector machine. In addition, we investigated the influence of lead time (0-2.0s) on accuracy. [Results] The quadratic and cubic support vector machine identified the activity of daily living and fall patterns more accurately than the linear support vector machine, and the cubic support vector machine was better for classification, although the difference was slight. The greatest accuracy for predicting the cause of the fall (87.9%) was obtained when the cubic support vector machine was used, activity of daily living was factored into the analysis, and the lead time was 0.25 sec. [Conclusion] Support vector machine can identify the cause of the fall during the pre-impact fall period. Appropriate individualized interventions may be designed based on the most likely cause of fall as identified by this analysis method.

Inducing unstable walking conditions through visual and auditory stimuli

“…To address this issue, it is essential to emphasize on the pre-fall condition to detect potential circumstances resulting into falls, thereby allowing third parties, such as physicians or caregivers, to take appropriate preventive measures. Our previous research involved the development of a system for detecting and classifying pre-fall conditions 4 ) , known as near-falls, using datasets such as SisFall 5 ) and KFall 6 ) . However, many previous studies, including ours, have the tendency to simulate falling movements or high-risk walking conditions in younger participants, potentially affecting the accuracy of the actual systems developed 7 ) .…”

The Effects of Nintendo Wii Fit on Postural Balance Control Training in the Geriatric Population: A Review

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