Department of Industrial and Systems Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA.
Department of Rehabilitation and Neurology, Unity Hospital, Rochester, NY 14626, USA.
Sensors (Basel). 2024 Jan 26;24(3):812. doi: 10.3390/s24030812.
Falls are common and dangerous for stroke survivors. Current fall risk assessment methods rely on subjective scales. Objective sensor-based methods could improve prediction accuracy.
Develop machine learning models using inertial sensors to objectively classify fall risk in stroke survivors. Determine optimal sensor configurations and clinical test protocols.
21 stroke survivors performed balance, Timed Up and Go, 10 Meter Walk, and Sit-to-Stand tests with and without dual-tasking. A total of 8 motion sensors captured lower limb and trunk kinematics, and 92 spatiotemporal gait and clinical features were extracted. Supervised models-Support Vector Machine, Logistic Regression, and Random Forest-were implemented to classify high vs. low fall risk. Sensor setups and test combinations were evaluated.
The Random Forest model achieved 91% accuracy using dual-task balance sway and Timed Up and Go walk time features. Single thorax sensor models performed similarly to multi-sensor models. Balance and Timed Up and Go best-predicted fall risk.
Machine learning models using minimal inertial sensors during clinical assessments can accurately quantify fall risk in stroke survivors. Single thorax sensor setups are effective. Findings demonstrate a feasible objective fall screening approach to assist rehabilitation.
对于脑卒中幸存者来说，跌倒很常见且很危险。目前的跌倒风险评估方法依赖于主观量表。基于客观传感器的方法可以提高预测准确性。
使用惯性传感器开发机器学习模型，客观地对脑卒中幸存者的跌倒风险进行分类。确定最佳传感器配置和临床测试方案。
21 名脑卒中幸存者进行了平衡、计时起立行走、10 米步行和坐站测试，同时进行和不进行双重任务。共 8 个运动传感器采集下肢和躯干运动学，提取了 92 个时空步态和临床特征。实现了监督模型-支持向量机、逻辑回归和随机森林，以对高风险与低风险跌倒进行分类。评估了传感器设置和测试组合。
随机森林模型使用双任务平衡摆动和计时起立行走行走时间特征实现了 91%的准确率。单胸传感器模型的表现与多传感器模型相似。平衡和计时起立行走最能预测跌倒风险。
使用临床评估期间的最小惯性传感器的机器学习模型可以准确量化脑卒中幸存者的跌倒风险。单胸传感器设置是有效的。研究结果表明，一种可行的客观跌倒筛查方法可辅助康复。
