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步态卫士:用于非侵入式干预系统的转向感知步态冻结检测

Gait-Guard: Turn-aware Freezing of Gait Detection for Non-intrusive Intervention Systems.

作者信息

Koltermann Kenneth, Clapham John, Blackwell GinaMari, Jung Woosub, Burnet Evie N, Gao Ye, Shao Huajie, Cloud Leslie, Pretzer-Aboff Ingrid, Zhou Gang

机构信息

Department of Computer Science, William & Mary.

School of Nursing, Virginia Commonwealth University.

出版信息

IEEE Int Conf Connect Health Appl Syst Eng Technol. 2024 Jun;2024:61-72. doi: 10.1109/chase60773.2024.00016. Epub 2024 Aug 5.

DOI:10.1109/chase60773.2024.00016
PMID:39262653
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11384236/
Abstract

Freezing of gait significantly reduces the quality of life for Parkinson's disease patients by increasing the risk of injurious falls and reducing mobility. Real-time intervention mechanisms promise relief from these symptoms, but require accurate real-time, portable freezing of gait detection systems to be effective. Current real-time detection systems have unacceptable false positive freezing of gait identification rates to be adopted by the patients for real-world use. To rectify this, we propose Gait-Guard, a closed-loop, real-time, and portable freezing of gait detection and intervention system that treats symptoms in real-time with a low false positive rate. We collected 1591 freezing of gait events across 26 patients to evaluate Gait-Guard. Gait-Guard achieved a 112% reduction in the false positive intervention rate when compared with other validated real-time freezing of gait detection systems, and detected 96.5% of the true positives with an average intervention latency of just 378.5ms in a subject-independent study, making Gait-Guard a practical system for patients to use in their daily lives.

摘要

冻结步态会显著降低帕金森病患者的生活质量,因为它会增加受伤跌倒的风险并降低行动能力。实时干预机制有望缓解这些症状,但需要准确的实时、便携式冻结步态检测系统才能有效。目前的实时检测系统的步态识别假阳性率过高,患者无法在现实生活中采用。为了纠正这一问题,我们提出了Gait-Guard,这是一种闭环、实时、便携式的冻结步态检测和干预系统,它能以低假阳性率实时治疗症状。我们收集了26名患者的1591次冻结步态事件来评估Gait-Guard。在一项独立于受试者的研究中,与其他经过验证的实时冻结步态检测系统相比,Gait-Guard的假阳性干预率降低了112%,检测出96.5%的真阳性,平均干预延迟仅为378.5毫秒,这使得Gait-Guard成为患者在日常生活中可以使用的实用系统。

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本文引用的文献

1
FoG-Finder: Real-time Freezing of Gait Detection and Treatment.步态冻结检测与治疗实时探测器(FoG-Finder)
IEEE Int Conf Connect Health Appl Syst Eng Technol. 2023 Jun;2023:22-33. Epub 2023 Jul 21.
2
Flexible Gel-Free Multi-Modal Wireless Sensors With Edge Deep Learning for Detecting and Alerting Freezing of Gait Symptom.具有边缘深度学习功能的灵活无凝胶多模态无线传感器,用于检测和预警步态冻结症状。
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高阶多项式变换器在步态检测精细冻结中的应用。
IEEE Trans Neural Netw Learn Syst. 2024 Sep;35(9):12746-12759. doi: 10.1109/TNNLS.2023.3264647. Epub 2024 Sep 3.
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An Explainable Spatial-Temporal Graphical Convolutional Network to Score Freezing of Gait in Parkinsonian Patients.一种可解释的时空图卷积神经网络,用于对帕金森病患者的步态冻结进行评分。
Sensors (Basel). 2023 Feb 4;23(4):1766. doi: 10.3390/s23041766.
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Automated freezing of gait assessment with marker-based motion capture and multi-stage spatial-temporal graph convolutional neural networks.基于标记的运动捕捉和多阶段时空图卷积神经网络的自动化冻结步态评估。
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Prediction of Freezing of Gait in Parkinson's Disease Using Unilateral and Bilateral Plantar-Pressure Data.利用单侧和双侧足底压力数据预测帕金森病患者的冻结步态
Front Neurol. 2022 Apr 28;13:831063. doi: 10.3389/fneur.2022.831063. eCollection 2022.
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Assessing inertial measurement unit locations for freezing of gait detection and patient preference.评估惯性测量单元位置以检测冻结步态和患者偏好。
J Neuroeng Rehabil. 2022 Feb 13;19(1):20. doi: 10.1186/s12984-022-00992-x.
9
Prediction and detection of freezing of gait in Parkinson's disease from plantar pressure data using long short-term memory neural-networks.使用长短时记忆神经网络从足底压力数据预测和检测帕金森病的冻结步态。
J Neuroeng Rehabil. 2021 Nov 27;18(1):167. doi: 10.1186/s12984-021-00958-5.
10
Graph Fusion Network-Based Multimodal Learning for Freezing of Gait Detection.基于图融合网络的步态冻结检测多模态学习
IEEE Trans Neural Netw Learn Syst. 2023 Mar;34(3):1588-1600. doi: 10.1109/TNNLS.2021.3105602. Epub 2023 Feb 28.