Identification of Characteristic Motor Patterns Preceding Freezing of Gait in Parkinson's Disease Using Wearable Sensors
Identification of Characteristic Motor Patterns Preceding Freezing of Gait in Parkinson's Disease Using Wearable Sensors
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DOI：
10.3389/fneur.2017.00394
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发表时间：
2017-08-14
影响因子：
3.4
通讯作者：
Chiari, Lorenzo
中科院分区：
文献类型：
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作者：
Palmerini, Luca;Rocchi, Laura;Chiari, Lorenzo
冻结步态 (FOG) 是晚期帕金森病 (PD) 患者中常见的一种致残症状。有节奏的听觉刺激等外部线索可以帮助经历僵硬的帕金森病患者恢复行走。最近开发了用于自动冻结检测的可穿戴系统。然而，这些系统会在 FOG 事件发生后对其进行检测。相反，在这项研究中，提出了一种预测 FOG（在实际发生之前）的新方法。 FOG 预测可能会提供预防性提示，从而降低发生 FOG 的可能性。此外，了解光纤陀螺的原因和情况仍然是一个开放的研究问题。因此，FOG 之前（FOG 前阶段）的运动模式的定量表征非常重要。在本研究中，使用可穿戴惯性传感器来识别和量化FOG前阶段的步态特征，并将其与FOG前阶段的步态特征进行比较。本研究的假设基于基于阈值的 FOG 模型，该模型表明在 FOG 发生之前，步态模式会发生退化。对 11 名 PD 受试者进行了分析。从惯性传感器记录的运动信号中提取的六个特征显示出步态与 FOG 前的显着差异。开发了一种分类算法，以测试预测 FOG 是否可行（即在发生之前检测到它）。分类程序的目的是识别 FOG 前阶段。结果证实，冻结前会出现步态退化。结果还为创建自动算法来预测 FOG 的可行性提供了初步证据。尽管存在一些局限性，但这项研究显示了表征和识别 FOG 前模式的有希望的发现，这是朝着更好地理解、预测和预防这种致残症状迈出的又一步。
Freezing of gait (FOG) is a disabling symptom that is common among patients with advanced Parkinson's disease (PD). External cues such as rhythmic auditory stimulation can help PD patients experiencing freezing to resume walking. Wearable systems for automatic freezing detection have been recently developed. However, these systems detect a FOG episode after it has happened. Instead, in this study, a new approach for the prediction of FOG (before it actually happens) is presented. Prediction of FOG might enable preventive cueing, reducing the likelihood that FOG will occur. Moreover, understanding the causes and circumstances of FOG is still an open research problem. Hence, a quantitative characterization of movement patterns just before FOG (the pre-FOG phase) is of great importance. In this study, wearable inertial sensors were used to identify and quantify the characteristics of gait during the pre-FOG phase and compare them with the characteristics of gait that do not precede FOG. The hypothesis of this study is based on the threshold-based model of FOG, which suggests that before FOG occurs, there is a degradation of the gait pattern. Eleven PD subjects were analyzed. Six features extracted from movement signals recorded by inertial sensors showed significant differences between gait and pre-FOG. A classification algorithm was developed in order to test if it is feasible to predict FOG (i.e., detect it before it happens). The aim of the classification procedure was to identify the pre-FOG phase. Results confirm that there is a degradation of gait occurring before freezing. Results also provide preliminary evidence on the feasibility of creating an automatic algorithm to predict FOG. Although some limitations are present, this study shows promising findings for characterizing and identifying pre-FOG patterns, another step toward a better understanding, prediction, and prevention of this disabling symptom.