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首页> 外文会议>IEEE International Conference on Machine Learning and Applications >A Study of Deep Learning for Predicting Freeze of Gait in Patients with Parkinson’s Disease
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A Study of Deep Learning for Predicting Freeze of Gait in Patients with Parkinson’s Disease

机译：对帕金森病患者冻结预测冻结的深度学习研究

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Freezing of gait (FOG) is a gait impairment, common in patients with advanced Parkinson’s disease. Predicting FOG before its onset enables preemptive cueing that can prevent FOG or reduce its intensity and duration. Deep learning models have recently been proposed to predict FOG. Such models have feature learning capabilities and do not require the use of hand-crafted features. However, some intricacies that are specific to this approach have not been carefully studied. In particular, the implication of the lack of accurately labelled pre-FOG data, which can have a significant impact on model development and evaluation, has not been fully understood.In this work, we discuss the challenges in deep learning for predicting FOG, illustrate the impact of the lack of accurate pre-FOG data on model development and evaluation, and present a more reliable evaluation method that is independent of the labelling of pre-FOG data. Using this new evaluation method, we study the deep learning schemes for FOG prediction by performing extensive experiments on a public domain dataset. The main conclusions of the study include the following: 1) even without accurate pre-FOG data, deep learning techniques can achieve very high FOG prediction performance while not introducing significant false alarms; 2) traditional deep learning performance metrics such as accuracy, sensitivity, and specificity may not be indicative of the FOG prediction performance; 3) human gait data have high subject-dependent variability, and it requires different deep learning models to achieve the best performance for different individuals; and finally 4) transfer learning is an effective technique for predicting FOG. To the best of our knowledge, this is the first research effort to derive these conclusions via extensive empirical analysis.
机译：步态（雾）冻结是一种步态障碍，伴有高级帕金森病的患者。预测其发作前的雾使得先发制人的提示可以防止雾或降低其强度和持续时间。最近已经提出了深入学习模型来预测雾。此类模型具有功能学习功能，不需要使用手工制作的功能。然而，没有仔细研究一些特定于这种方法的复杂性。特别是，缺乏准确标记的预雾数据的含义，这可能对模型开发和评估产生重大影响，尚未完全理解。在这项工作中，我们讨论了预测雾的深度学习挑战缺乏准确的雾化数据对模型开发和评估的影响，并提出了一种更可靠的评估方法，这些方法与雾前数据的标签无关。使用这种新的评估方法，我们研究通过对公共域数据集进行广泛的实验来研究迷雾预测的深度学习方案。该研究的主要结论包括以下内容：1）即使没有准确的预FOG数据，深度学习技术能达到非常高的FOG预测的性能，同时不会引入显著误报警; 2）传统的深度学习性能指标，如准确性，灵敏度和特异性可能不指示雾预测性能; 3）人体步态数据具有高的对象依赖的变化，它需要不同的深度学习模式，实现对不同个体的最佳性能;最后4）转移学习是一种预测雾的有效技术。据我们所知，这是通过广泛的实证分析来得出这些结论的第一个研究努力。

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