Freezing of gait (FoG) is a common motor dysfunction in individuals with Parkinson’s disease. FoG impairs walking and is associated with increased fall risk. On-demand external cueing systems can detect FoG and provide stimuli to help individuals overcome freezing. Predicting FoG before onset enables preemptive cueing and may prevent FoG. However, detection and prediction remain challenging. If FoG data are not available for an individual, patient-independent models have been used to detect FoG, which have shown great sensitivity and poor specificity, or vice versa. In this study, we introduce a Deep Gait Anomaly Detector (DGAD) using a transfer learning-based approach to improve FoG detection accuracy. We also evaluate the effect of data augmentation and additional pre-FoG segments on prediction rate. Seven individuals with PD performed a series of daily walking tasks wearing inertial measurement units on their ankles. The DGAD algorithm demonstrated average sensitivity and specificity of 63.0% and 98.6% (3.2% improvement compared with the highest specificity in the literature). The target models identified 87.4% of FoG onsets, with 21.9% predicted. This study demonstrates our algorithm’s potential for accurate identification of FoG and delivery of cues for patients for whom no FoG data is available for model training.

中文翻译：

---

实时预测帕金森病患者步态冻结：一种新型的深度一类分类器

步态冻结 (FoG) 是帕金森病患者常见的运动功能障碍。FoG 会影响步行，并与跌倒风险增加有关。按需外部提示系统可以检测 FoG 并提供刺激以帮助个人克服冻结。在发病前预测 FoG 可实现先发制人的提示，并可能防止 FoG。然而，检测和预测仍然具有挑战性。如果个人无法获得 FoG 数据，则已使用独立于患者的模型来检测 FoG，这些模型显示出很高的敏感性和较差的特异性，反之亦然。在这项研究中，我们引入了一种深度步态异常检测器 (DGAD)，该检测器使用基于迁移学习的方法来提高 FoG 检测精度。我们还评估了数据增强和额外的 pre-FoG 片段对预测率的影响。七名患有 PD 的人在脚踝上佩戴惯性测量装置进行了一系列日常步行任务。DGAD 算法的平均敏感性和特异性分别为 63.0% 和 98.6%（与文献中最高的特异性相比提高了 3.2%）。目标模型确定了 87.4% 的 FoG 发作，预测为 21.9%。这项研究证明了我们的算法在准确识别 FoG 和为没有 FoG 数据可用于模型训练的患者提供线索方面的潜力。