Abstract：
BACKGROUND: The significance of diagnosing illnesses associated with brain cognitive and gait freezing phase patterns has led to a recent surge in interest in the study of gait for mental disorders. A more precise and effective way to characterize and classify many common gait problems, such as foot and brain pulse disorders, can improve prognosis evaluation and treatment options for Parkinson patients. Nonetheless, the primary clinical technique for assessing gait abnormalities at the moment is visual inspection, which depends on the subjectivity of the observer and can be inaccurate.
OBJECTIVE: This study investigates whether it is possible to differentiate between gait brain disorder and the typical walking pattern using machine learning driven supervised learning techniques and data obtained from inertial measurement unit sensors for brain, hip and leg rehabilitation.
METHODS: The proposed method makes use of the Daphnet freezing of Gait Data Set, consisted of 237 instances with 9 attributes. The method utilizes machine learning and feature reduction approaches in leg and hip gait recognition.
RESULTS: From the obtained results, it is concluded that among all classifiers RF achieved highest accuracy as 98.9 % and Perceptron achieved lowest i.e. 70.4 % accuracy. While utilizing LDA as feature reduction approach, KNN, RF and NB also achieved promising accuracy and F1-score in comparison with SVM and LR classifiers.
CONCLUSIONS: In order to distinguish between the different gait disorders associated with brain tissues freezing/non-freezing and normal walking gait patterns, it is shown that the integration of different machine learning algorithms offers a viable and prospective solution. This research implies the need for an impartial approach to support clinical judgment.
OBJECTIVE: This study investigates whether it is possible to differentiate between gait brain disorder and the typical walking pattern using machine learning driven supervised learning techniques and data obtained from inertial measurement unit sensors for brain, hip and leg rehabilitation.
METHODS: The proposed method makes use of the Daphnet freezing of Gait Data Set, consisted of 237 instances with 9 attributes. The method utilizes machine learning and feature reduction approaches in leg and hip gait recognition.
RESULTS: From the obtained results, it is concluded that among all classifiers RF achieved highest accuracy as 98.9 % and Perceptron achieved lowest i.e. 70.4 % accuracy. While utilizing LDA as feature reduction approach, KNN, RF and NB also achieved promising accuracy and F1-score in comparison with SVM and LR classifiers.
CONCLUSIONS: In order to distinguish between the different gait disorders associated with brain tissues freezing/non-freezing and normal walking gait patterns, it is shown that the integration of different machine learning algorithms offers a viable and prospective solution. This research implies the need for an impartial approach to support clinical judgment.
摘要：
背景：诊断与大脑认知和步态冻结阶段模式相关的疾病的重要性导致了最近对精神障碍步态研究的兴趣激增。一种更精确和有效的方法来表征和分类许多常见的步态问题，如脚和脑脉搏障碍，可以改善帕金森病患者的预后评估和治疗选择。尽管如此，目前评估步态异常的主要临床技术是视觉检查，这取决于观察者的主观性，可能是不准确的。
目的：这项研究调查了是否可以使用机器学习驱动的监督学习技术和从大脑惯性测量单元传感器获得的数据来区分步态脑障碍和典型的步行模式。臀部和腿部康复。
方法：所提出的方法利用了步态数据集的Daphnet冻结，由237个具有9个属性的实例组成。该方法在腿部和臀部步态识别中利用机器学习和特征减少方法。
结果：从获得的结果来看，结论是，在所有分类器中，RF达到了最高的准确率，为98.9%，感知器达到了最低的准确率，即70.4%。在利用LDA作为特征缩减方法的同时，KNN,与SVM和LR分类器相比，RF和NB也实现了有希望的准确性和F1得分。
结论：为了区分与脑组织冻结/非冻结和正常行走步态模式相关的不同步态障碍，这表明，不同的机器学习算法的集成提供了一个可行的和前瞻性的解决方案。这项研究意味着需要一种公正的方法来支持临床判断。
目的：这项研究调查了是否可以使用机器学习驱动的监督学习技术和从大脑惯性测量单元传感器获得的数据来区分步态脑障碍和典型的步行模式。臀部和腿部康复。
方法：所提出的方法利用了步态数据集的Daphnet冻结，由237个具有9个属性的实例组成。该方法在腿部和臀部步态识别中利用机器学习和特征减少方法。
结果：从获得的结果来看，结论是，在所有分类器中，RF达到了最高的准确率，为98.9%，感知器达到了最低的准确率，即70.4%。在利用LDA作为特征缩减方法的同时，KNN,与SVM和LR分类器相比，RF和NB也实现了有希望的准确性和F1得分。
结论：为了区分与脑组织冻结/非冻结和正常行走步态模式相关的不同步态障碍，这表明，不同的机器学习算法的集成提供了一个可行的和前瞻性的解决方案。这项研究意味着需要一种公正的方法来支持临床判断。
