Abstract：
BACKGROUND: Parkinson\'s Disease (PD) demands early diagnosis and frequent assessment of symptoms. In particular, analysing hand movements is pivotal to understand disease progression. Advancements in hand tracking using Deep Learning (DL) allow for the automatic and objective disease evaluation from video recordings of standardised motor tasks, which are the foundation of neurological examinations. In view of this scenario, this narrative review aims to describe the state of the art and the future perspective of DL frameworks for hand tracking in video-based PD assessment.
METHODS: A rigorous search of PubMed, Web of Science, IEEE Explorer, and Scopus until October 2023 using primary keywords such as parkinson, hand tracking, and deep learning was performed to select eligible by focusing on video-based PD assessment through DL-driven hand tracking frameworks RESULTS:: After accurate screening, 23 publications met the selection criteria. These studies used various solutions, from well-established pose estimation frameworks, like OpenPose and MediaPipe, to custom deep architectures designed to accurately track hand and finger movements and extract relevant disease features. Estimated hand tracking data were then used to differentiate PD patients from healthy individuals, characterise symptoms such as tremors and bradykinesia, or regress the Movement Disorder Society-Unified Parkinson\'s Disease Rating Scale (MDS-UPDRS) by automatically assessing clinical tasks such as finger tapping, hand movements, and pronation-supination.
CONCLUSIONS: DL-driven hand tracking holds promise for PD assessment, offering precise, objective measurements for early diagnosis and monitoring, especially in a telemedicine scenario. However, to ensure clinical acceptance, standardisation and validation are crucial. Future research should prioritise large open datasets, rigorous validation on patients, and the investigation of new frontiers such as tracking hand-hand and hand-object interactions for daily-life tasks assessment.
METHODS: A rigorous search of PubMed, Web of Science, IEEE Explorer, and Scopus until October 2023 using primary keywords such as parkinson, hand tracking, and deep learning was performed to select eligible by focusing on video-based PD assessment through DL-driven hand tracking frameworks RESULTS:: After accurate screening, 23 publications met the selection criteria. These studies used various solutions, from well-established pose estimation frameworks, like OpenPose and MediaPipe, to custom deep architectures designed to accurately track hand and finger movements and extract relevant disease features. Estimated hand tracking data were then used to differentiate PD patients from healthy individuals, characterise symptoms such as tremors and bradykinesia, or regress the Movement Disorder Society-Unified Parkinson\'s Disease Rating Scale (MDS-UPDRS) by automatically assessing clinical tasks such as finger tapping, hand movements, and pronation-supination.
CONCLUSIONS: DL-driven hand tracking holds promise for PD assessment, offering precise, objective measurements for early diagnosis and monitoring, especially in a telemedicine scenario. However, to ensure clinical acceptance, standardisation and validation are crucial. Future research should prioritise large open datasets, rigorous validation on patients, and the investigation of new frontiers such as tracking hand-hand and hand-object interactions for daily-life tasks assessment.
摘要：
背景：帕金森病（PD）需要早期诊断和频繁评估症状。特别是,分析手部动作对于了解疾病进展至关重要。使用深度学习（DL）的手跟踪技术的进步允许从标准化运动任务的视频记录中自动和客观地评估疾病，这是神经系统检查的基础。鉴于这种情况，这篇叙述性综述旨在描述基于视频的PD评估中用于手跟踪的DL框架的最新技术和未来前景。
方法：对PubMed的严格搜索，WebofScience,IEEEExplorer,和Scopus，直到2023年10月使用主要关键字，如Parkinson，手跟踪,和深度学习是通过DL驱动的手跟踪框架专注于基于视频的PD评估来选择符合条件的结果：：经过准确的筛选，23份出版物符合选择标准。这些研究使用了各种解决方案，从完善的姿态估计框架，比如OpenPose和MediaPipe，自定义深度架构，旨在准确跟踪手和手指的运动，并提取相关的疾病特征。然后使用估计的手跟踪数据来区分PD患者和健康个体，表征症状，如震颤和运动迟缓，或通过自动评估临床任务，如手指敲击，回归运动障碍社会统一帕金森病评定量表（MDS-UPDRS），手部动作,和旋前旋后。
结论：DL驱动的手跟踪有望用于PD评估，提供精确的，用于早期诊断和监测的客观测量，尤其是在远程医疗场景中。然而,为了确保临床接受，标准化和验证至关重要。未来的研究应该优先考虑大型开放数据集，对患者进行严格的验证，以及对新领域的调查，例如跟踪手-手和手-物相互作用以进行日常生活任务评估。
方法：对PubMed的严格搜索，WebofScience,IEEEExplorer,和Scopus，直到2023年10月使用主要关键字，如Parkinson，手跟踪,和深度学习是通过DL驱动的手跟踪框架专注于基于视频的PD评估来选择符合条件的结果：：经过准确的筛选，23份出版物符合选择标准。这些研究使用了各种解决方案，从完善的姿态估计框架，比如OpenPose和MediaPipe，自定义深度架构，旨在准确跟踪手和手指的运动，并提取相关的疾病特征。然后使用估计的手跟踪数据来区分PD患者和健康个体，表征症状，如震颤和运动迟缓，或通过自动评估临床任务，如手指敲击，回归运动障碍社会统一帕金森病评定量表（MDS-UPDRS），手部动作,和旋前旋后。
结论：DL驱动的手跟踪有望用于PD评估，提供精确的，用于早期诊断和监测的客观测量，尤其是在远程医疗场景中。然而,为了确保临床接受，标准化和验证至关重要。未来的研究应该优先考虑大型开放数据集，对患者进行严格的验证，以及对新领域的调查，例如跟踪手-手和手-物相互作用以进行日常生活任务评估。
