Freezing of gait (FOG) is one of the most troublesome symptoms of Parkinson’s disease, affecting more than 50% of patients in advanced stages of the disease. Wearable technology has been widely used for its automatic detection, and some papers have been recently published in the direction of its prediction. Such predictions may be used for the administration of cues, in order to prevent the occurrence of gait freezing. The aim of the present study was to propose a wearable system able to catch the typical degradation of the walking pattern preceding FOG episodes, to achieve reliable FOG prediction using machine learning algorithms and verify whether dopaminergic therapy affects the ability of our system to detect and predict FOG. Methods: A cohort of 11 Parkinson’s disease patients receiving (on) and not receiving (off) dopaminergic therapy was equipped with two inertial sensors placed on each shin, and asked to perform a timed up and go test. We performed a step-to-step segmentation of the angular velocity signals and subsequent feature extraction from both time and frequency domains. We employed a wrapper approach for feature selection and optimized different machine learning classifiers in order to catch FOG and pre-FOG episodes. Results: The implemented FOG detection algorithm achieved excellent performance in a leave-one-subject-out validation, in patients both on and off therapy. As for pre-FOG detection, the implemented classification algorithm achieved 84.1% (85.5%) sensitivity, 85.9% (86.3%) specificity and 85.5% (86.1%) accuracy in leave-one-subject-out validation, in patients on (off) therapy. When the classification model was trained with data from patients on (off) and tested on patients off (on), we found 84.0% (56.6%) sensitivity, 88.3% (92.5%) specificity and 87.4% (86.3%) accuracy. Conclusions: Machine learning models are capable of predicting FOG before its actual occurrence with adequate accuracy. The dopaminergic therapy affects pre-FOG gait patterns, thereby influencing the algorithm’s effectiveness.

中文翻译：

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可穿戴设备和机器学习预测帕金森氏症步态冻结

步态冻结（FOG）是帕金森氏病最棘手的症状之一，影响了疾病晚期的50％以上的患者。可穿戴技术已被广泛用于其自动检测，并且最近在其预测方向上发表了一些论文。这样的预测可用于提示的管理，以防止步态冻结的发生。本研究的目的是提出一种可穿戴系统，该系统能够捕获FOG发作之前行走模式的典型退化，使用机器学习算法实现可靠的FOG预测，并验证多巴胺能疗法是否会影响我们系统检测和预测的能力多雾路段。方法：一组接受（接受）多巴胺能治疗和未接受（接受）多巴胺能治疗的11名帕金森氏病患者在每只胫骨上均配备了两个惯性传感器，并要求他们进行定时上门测试。我们对角速度信号进行了逐步分割，并随后从时域和频域提取了特征。我们采用了包装方法来进行特征选择，并优化了不同的机器学习分类器，以捕捉FOG和FOG之前的事件。结果：实施的FOG检测算法在上下一次治疗中均获得了优异的性能，无论患者是否接受治疗。对于FOG之前的检测，实施的分类算法在留一法验证中达到了84.1％（85.5％）的灵敏度，85.9％（86.3％）的特异性和85.5％（86.1％）的准确性，在（非）治疗患者中。当使用来自（开）患者的数据训练分类模型并在（开）患者进行测试时，我们发现灵敏度为84.0％（56.6％），特异性为88.3％（92.5％），准确度为87.4％（86.3％）。结论：机器学习模型能够在FOG实际发生之前以足够的准确性进行预测。多巴胺能疗法会影响FOG前的步态，从而影响算法的有效性。