Freezing of Gait (FoG) is a motor symptom of Parkinson's disease (PD) that frequently occurs in the long-term sufferers of the disease. FoG may result to nursing home admission as it can lead to falls, and therefore, it impacts negatively on the quality of life. The focus of this study is the systematic evaluation of machine learning techniques in conjunction with varying size time windows and time/frequency domain feature sets in predicting a FoG event before its onset. In the experiments, the Daphnet FoG dataset is used to benchmark performance. This consists of accelerometer signals obtained from sensors mounted on the ankle, thigh and trunk of the PD patients. The dataset is annotated with instances of normal activity events, and FoG events. To predict the onset of FoG, the dataset is augmented with an additional class, termed ‘transition’, which relates to a manually defined period prior to the occurrence of a FoG episode. In this research, five machine learning models are used, namely, Random Forest, Extreme Gradient Boosting, Gradient Boosting, Support Vector Machines using Radial Basis Functions, and Neural Networks. Support Vector Machines with Radial Basis kernels provided the best performance achieving sensitivity values of 72.34%, 91.49%, 75.00%, and specificity values of 87.36%, 88.51% and 93.62%, for the FoG, transition and normal activity classes, respectively.

步态冻结（FoG）是帕金森氏病（PD）的运动症状，长期发生在帕金森氏病的患者中。FoG可能导致入院，因为它可能导致跌倒，因此对生活质量产生负面影响。这项研究的重点是对机器学习技术的系统评估，并结合各种大小的时间窗口和时/频域特征集，以预测FoG事件发作之前的时间。在实验中，Daphnet FoG数据集用于基准性能。这包括从安装在PD患者脚踝，大腿和躯干上的传感器获得的加速度计信号。数据集带有正常活动事件和FoG事件的实例注释。为了预测FoG的发生，数据集会增加一个称为“过渡”的类，这与发生FoG事件之前的手动定义的时间段有关。在这项研究中，使用了五个机器学习模型，即随机森林，极端梯度提升，梯度提升，使用径向基函数的支持向量机和神经网络。支持向量机具有径向基核，对于FoG，过渡和正常活动类别，灵敏度分别为72.34％，91.49％，75.00％和特异性值为87.36％，88.51％和93.62％，提供了最佳性能。