DeepFoG: An IMU-Based Detection of Freezing of Gait Episodes in Parkinson's Disease Patients via Deep Learning.
DeepFoG: An IMU-Based Detection of Freezing of Gait Episodes in Parkinson's Disease Patients via Deep Learning.
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DeepFoG：通过深度学习检测帕金森病患者步态异常的基于IMU的检测。
DOI：
10.3389/frobt.2021.537384
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发表时间：
2021
影响因子：
3.4
通讯作者：
Hadjileontiadis LJ
中科院分区：
文献类型：
--
作者：
Bikias T;Iakovakis D;Hadjidimitriou S;Charisis V;Hadjileontiadis LJ
步态冻结(雾)是一种运动障碍，主要出现在帕金森氏病(PD)的晚期。它导致不能行走，尽管帕金森病患者的意图，导致失去协调性，增加跌倒和受伤的风险，并严重影响帕金森病患者的生活质量。压力、情绪刺激和多任务处理与雾发作的出现有关，而患者的功能和自信心也在不断恶化。通过对惯性测量单元(IMU)数据的分析，提出了一种非侵入性的雾事件检测方法。具体地说，11名PD受试者的加速度计和陀螺仪数据通过深度学习进行处理，用于基于窗口的雾事件检测，这些数据是在连续行走过程中从单个手腕佩戴的IMU传感器捕获的。该方法，即DeepFoG，以留一主题排除(Loso)交叉验证(CV)和10倍交叉验证(CV)的方式对其正确估计每个数据窗口是否存在雾事件的能力进行了评估。实验结果表明，DeepFoG对Loso CV和10倍CV方案的敏感度/特异度分别达到83%/88%和86%/90%，取得了令人满意的效果。DeepFoG的良好性能揭示了基于单臂IMU的实时雾检测的潜力，可以通过节奏听觉刺激(RAS)和手部振动等刺激来指导有效的干预。通过这种方式，DeepFoG可能会为PD患者消除跌倒风险，维持他们在日常生活活动中的生活质量。
Freezing of Gait (FoG) is a movement disorder that mostly appears in the late stages of Parkinson’s Disease (PD). It causes incapability of walking, despite the PD patient’s intention, resulting in loss of coordination that increases the risk of falls and injuries and severely affects the PD patient’s quality of life. Stress, emotional stimulus, and multitasking have been encountered to be associated with the appearance of FoG episodes, while the patient’s functionality and self-confidence are constantly deteriorating. This study suggests a non-invasive method for detecting FoG episodes, by analyzing inertial measurement unit (IMU) data. Specifically, accelerometer and gyroscope data from 11 PD subjects, as captured from a single wrist-worn IMU sensor during continuous walking, are processed via Deep Learning for window-based detection of the FoG events. The proposed approach, namely DeepFoG, was evaluated in a Leave-One-Subject-Out (LOSO) cross-validation (CV) and 10-fold CV fashion schemes against its ability to correctly estimate the existence or not of a FoG episode at each data window. Experimental results have shown that DeepFoG performs satisfactorily, as it achieves 83%/88% and 86%/90% sensitivity/specificity, for LOSO CV and 10-fold CV schemes, respectively. The promising performance of the proposed DeepFoG reveals the potentiality of single-arm IMU-based real-time FoG detection that could guide effective interventions via stimuli, such as rhythmic auditory stimulation (RAS) and hand vibration. In this way, DeepFoG may scaffold the elimination of risk of falls in PD patients, sustaining their quality of life in everyday living activities.
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导致步态冻结的病变定位于小脑功能网络。
影响因子：
11.2
作者：
Fasano A;Laganiere SE;Lam S;Fox MD
通讯作者：
Fox MD
影响因子：
4.6
作者：
Bella SD;Benoit CE;Farrugia N;Keller PE;Obrig H;Mainka S;Kotz SA
通讯作者：
Kotz SA
影响因子：
3.4
作者：
Hausdorff, Jeffrey M.;Lowenthal, Justine;Giladi, Nir
通讯作者：
Giladi, Nir
影响因子：
3.4
作者：
Palmerini, Luca;Rocchi, Laura;Chiari, Lorenzo
通讯作者：
Chiari, Lorenzo
影响因子：
14.5
作者：
Pozzi, Nicolo G.;Canessa, Andrea;Isaias, Ioannis U.
通讯作者：
Isaias, Ioannis U.