Freezing of gait (FoG) prediction, combined with rhythmic laser cues, may help Parkinson's disease (PD) patients overcome FoG episodes. This study aimed to utilize the impaired gait patterns preceding FoG to build a machine-learning-based model for FoG prediction. Acceleration signals were collected using an accelerometer attached to the lower back of 12 PD patients with FoG while they were performing designed FoG-provoking walking tasks. Step-based impaired gait features and conventional FoG detection features were extracted from the signals, based on which two FoG prediction models were built using AdaBoost to validate if the use of the impaired gait features can better predict FoG. For the correct labeling of the gait prior to FoG (pre-FoG), the personalized pre-FoG phase was defined according to the slope of the impaired gait pattern. The impaired gait features were relabeled based on the pre-FoG phase upon which the personalized labeled FoG prediction model was built. This was compared with the model built using unified labeling. Results showed that impaired gait features could better predict FoG than conventional FoG detection features with low time latency, and personalized labeling could further improve the FoG prediction accuracy. Using impaired gait features and personalized labeling, we built a FoG prediction model with 0.93 sec of latency. Compared to using conventional features and unified labeling, our model achieved 5.7% higher accuracy (82.7%) in patient-dependent test and 9.8% higher accuracy (77.9%) in patient-independent test. Therefore, using the impaired gait patterns is a promising approach to accurately predict FoG with low latency.

中文翻译：

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通过识别受损的步态模式来预测帕金森氏病患者的步态冻结。

步态冻结（FoG）预测结合有节奏的激光提示可能有助于帕金森病（PD）患者克服FoG发作。这项研究旨在利用FoG之前受损的步态模式来建立基于机器学习的FoG预测模型。当12名PD患者接受FoG刺激的步行任务时，使用附在其下背部的加速度计收集加速度信号。从信号中提取基于步态的步态受损特征和传统的FoG检测特征，然后使用AdaBoost建立两个FoG预测模型，以验证使用步态受损的特征是否可以更好地预测FoG。为了在FoG之前正确标记步态（pre-FoG），根据受损步态模式的斜率定义个​​性化的pre-FoG阶段。根据前FoG阶段重新标记受损的步态特征，在此阶段建立个性化标记的FoG预测模型。将此与使用统一标签构建的模型进行了比较。结果表明，步态受损的功能比低时延的传统FoG检测功能可以更好地预测FoG，个性化标记可以进一步提高FoG预测的准确性。利用受损的步态功能和个性化标签，我们构建了具有0.93秒延迟的FoG预测模型。与使用常规功能和统一标记相比，我们的模型在患者相关测试中的准确度提高了5.7％（82.7％），在患者独立测试中的准确度提高了9.8％（77.9％）。因此，使用受损的步态模式是一种以低等待时间准确预测FoG的有前途的方法。