Lee Chia-Hsuan, Mendoza Tomas, Huang Chien-Hua, Sun Tien-Lung
Department of Data Science, Soochow University, No. 70, Linxi Road, Shilin District, Taipei 111, Taiwan.
Department of Industrial Engineering and Management, Yuan Ze University, 135 Yuan Tung Road, Chungli District, Taoyuan 320, Taiwan.
Healthcare (Basel). 2023 Jul 5;11(13):1938. doi: 10.3390/healthcare11131938.
Fall-risk assessment studies generally focus on identifying characteristics that affect postural balance in a specific group of subjects. However, falls affect a multitude of individuals. Among the groups with the most recurrent fallers are the community-dwelling elderly and stroke survivors. Thus, this study focuses on identifying a set of features that can explain fall risk for these two groups of subjects. Sixty-five community dwelling elderly (forty-nine female, sixteen male) and thirty-five stroke-survivors (twenty-two male, thirteen male) participated in our study. With the use of an inertial sensor, some features are extracted from the acceleration data of a Timed Up and Go (TUG) test performed by both groups of individuals. A short-form berg balance scale (SFBBS) score and the TUG test score were used for labeling the data. With the use of a 100-fold cross-validation approach, Relief-F and Extra Trees Classifier algorithms were used to extract sets of the top 5, 10, 15, 20, 25, and 30 features. Random Forest classifiers were trained for each set of features. The best models were selected, and the repeated features for each group of subjects were analyzed and discussed. The results show that only the stand duration was an important feature for the prediction of fall risk across all clinical tests and both groups of individuals.
跌倒风险评估研究通常侧重于识别影响特定受试者群体姿势平衡的特征。然而，跌倒影响众多个体。跌倒最频繁的群体包括社区居住的老年人和中风幸存者。因此，本研究侧重于识别一组能够解释这两组受试者跌倒风险的特征。65名社区居住的老年人（49名女性，16名男性）和35名中风幸存者（22名男性，13名女性）参与了我们的研究。通过使用惯性传感器，从两组个体进行的定时起立行走（TUG）测试的加速度数据中提取了一些特征。使用简短伯格平衡量表（SFBBS）评分和TUG测试评分对数据进行标注。采用100倍交叉验证方法，使用Relief-F和Extra Trees Classifier算法提取排名前5、10、15、20、25和30的特征集。针对每组特征训练随机森林分类器。选择最佳模型，并对每组受试者重复出现的特征进行分析和讨论。结果表明，在所有临床试验和两组个体中，只有站立持续时间是预测跌倒风险的重要特征。
