A multifactorial fall risk assessment system for older people utilizing a low-cost, markerless Microsoft Kinect

Background: Falls among older people are a major public health concern due to their high prevalence and serious consequences. Accurate, convenient, and cost-effective assessment of fall risk is an essential first step for effective fall intervention and prevention. This study aims to develop a multifactorial fall risk assessment system for older people using a low-cost, markerless Microsoft Kinect. Methods: : A Kinect-based test battery was designed and implemented in Unity3D to comprehensively assess major fall risk factors on physiological, psychological, and integrated functions. A follow-up experiment was conducted with 102 community-dwelling Korean older women to assess their fall risks and to investigate their prospective falls during a 6-month follow-up period. The participants were divided into a high fall risk group (N=22) and a low fall risk group (N=80) based on prospective falls. Based on 11 variables with significant differences between the two groups, random forest-based machine learning classification models were further constructed to classify the fall risk. Results: : Experimental results showed that the high fall risk group performed significantly worse on the Kinect-based test battery, especially in Sit to Stand 5 times (STS5), Choice Stepping Reaction Test (CSRT), and Fall Efficacy Scale (FES). The random forest classification model achieved average classification accuracy of 84.7% with 83.3% sensitivity and 86.1% specificity. In addition, the individual's performance was computed as the percentile value of a normative database so that deficiencies can be clearly visualized and targeted for intervention. Conclusion: These findings indicate that the developed low-cost, markerless Kinect-based multifactorial fall risk assessment system can not only accurately screen out ‘at risk’ older individuals, but also identify potential fall risk factors. The developed system has good potential for effective fall intervention and prevention.