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Objective: Falls are often accompanied by huge social costs, and fall risk assessment is essential to protect the elderly from serious injuries and reduce financial burdens. The standard timed up and go (TUG) balance assessment test focuses on the total walking time and scenarios without environmental changes, which is flawed in providing rich information related to falls and evaluating the gait adaptability in response to environmental changes. Therefore, a fall risk assessment system that relies on a variable environment is actually needed. Methods: We have constructed an environment-adaptive TUG (EATUG) test system with three terrain surfaces (levels/obstacles/stairs). One hundred and three elderly from Shenzhen Luohu Hospital is recruited to participate in the experiment. The wearable inertial sensors attached to the two shanks are used to acquire data, and the gait parameters that may be related to falls are extracted and quantified. Results: Most of the parameters have significant differences between the high-risk group and the low-risk group (e.g., peak power, maximum radius, double support, etc., p <; 0.001). In addition, the average sensitivity and specificity of fall risk prediction are 85.7% and 92.9%, while the average accuracy is 9.52% higher than the standard TUG test. Conclusion: The EATUG test system can provide richer gait characteristics and fall-related information, which is a good improvement on the drawbacks of the standard TUG test. Significance: The proposed test system is expected to replace the standard TUG test and be used for fall screening of high-risk elderly in the community to reduce the occurrence of falls.

A Novel Environment-Adaptive Timed Up and Go Test System for Fall Risk Assessment With Wearable Inertial Sensors