Schumann Paula, Scholz Maria, Trentzsch Katrin, Jochim Thurid, Śliwiński Grzegorz, Malberg Hagen, Ziemssen Tjalf
Institute of Biomedical Engineering, TU Dresden, Fetscherstr. 29, 01307 Dresden, Germany.
Center of Clinical Neuroscience, Neurological Clinic, University Hospital Carl Gustav Carus, TU Dresden, Fetscherstr. 74, 01307 Dresden, Germany.
Brain Sci. 2022 Oct 31;12(11):1477. doi: 10.3390/brainsci12111477.
One of the common causes of falls in people with Multiple Sclerosis (pwMS) is walking impairment. Therefore, assessment of gait is of importance in MS. Gait analysis and fall detection can take place in the clinical context using a wide variety of available methods. However, combining these methods while using machine learning algorithms for detecting falls has not been performed. Our objective was to determine the most relevant method for determining fall risk by analyzing eleven different gait data sets with machine learning algorithms. In addition, we examined the most important features of fall detection. A new feature selection ensemble (FS-Ensemble) and four classification models (Gaussian Naive Bayes, Decision Tree, k-Nearest Neighbor, Support Vector Machine) were used. The FS-Ensemble consisted of four filter methods: Chi-square test, information gain, Minimum Redundancy Maximum Relevance and RelieF. Various thresholds (50%, 25% and 10%) and combination methods (Union, Union 2, Union 3 and Intersection) were examined. Patient-reported outcomes using specialized walking questionnaires such as the 12-item Multiple Sclerosis Walking Scale (MSWS-12) and the Early Mobility Impairment Questionnaire (EMIQ) achieved the best performances with an F1 score of 0.54 for detecting falls. A combination of selected features of MSWS-12 and EMIQ, including the estimation of walking, running and stair climbing ability, the subjective effort as well as necessary concentration and walking fluency during walking, the frequency of stumbling and the indication of avoidance of social activity achieved the best recall of 75%. The Gaussian Naive Bayes was the best classification model for detecting falls with almost all data sets. FS-Ensemble improved the classification models and is an appropriate technique for reducing data sets with a large number of features. Future research on other risk factors, such as fear of falling, could provide further insights.
多发性硬化症患者（pwMS）跌倒的常见原因之一是行走障碍。因此，步态评估在多发性硬化症中至关重要。步态分析和跌倒检测可以在临床环境中使用多种可用方法进行。然而，在使用机器学习算法检测跌倒时，尚未将这些方法结合起来。我们的目标是通过使用机器学习算法分析11个不同的步态数据集，确定最相关的跌倒风险判定方法。此外，我们研究了跌倒检测的最重要特征。使用了一种新的特征选择集成方法（FS-Ensemble）和四种分类模型（高斯朴素贝叶斯、决策树、k近邻、支持向量机）。FS-Ensemble由四种过滤方法组成：卡方检验、信息增益、最小冗余最大相关性和RelieF。研究了各种阈值（50%、25%和10%）和组合方法（并集、并集2、并集3和交集）。使用专门的步行问卷（如12项多发性硬化症步行量表（MSWS-12）和早期运动障碍问卷（EMIQ））得出的患者报告结果在检测跌倒方面表现最佳，F1得分为0.54。MSWS-12和EMIQ的选定特征组合，包括对步行、跑步和爬楼梯能力的估计、主观努力以及步行时必要的注意力和步行流畅性、绊倒频率和避免社交活动的指征，实现了75%的最佳召回率。高斯朴素贝叶斯是几乎所有数据集检测跌倒的最佳分类模型。FS-Ensemble改进了分类模型，是一种减少具有大量特征的数据集的合适技术。未来对其他风险因素（如害怕跌倒）的研究可能会提供进一步的见解。
