Pose-Based Tremor Classification for Parkinson’s Disease Diagnosis from Video

Zhang, Haozheng; Ho, Edmond S. L.; Zhang, Xiatian; Shum, Hubert P. H.

doi:10.1007/978-3-031-16440-8_47

Cited by 7 publications

(3 citation statements)

References 30 publications

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“…In some studies [21][22][23], PD severity has been assessed through videos, thanks to the technological advances with CV systems. For example, Zhang et al [21] focused on tremors, while Lu et al [22,23] sought to quantify the severity of PD through the analysis of videos showing patients performing MDS-UPDRS. Zhang et al [21] focused their research in analyzing tremor severity.…”

Section: Related Workmentioning

confidence: 99%

“…For example, Zhang et al [21] focused on tremors, while Lu et al [22,23] sought to quantify the severity of PD through the analysis of videos showing patients performing MDS-UPDRS. Zhang et al [21] focused their research in analyzing tremor severity. For this purpose, they used OpenPose [24] for extracting 2D skeleton features; after this step, classification was performed using a graph neural network with a spatial attention mechanism.…”

Section: Related Workmentioning

confidence: 99%

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Computer Vision for Parkinson’s Disease Evaluation: A Survey on Finger Tapping

Amo-Salas,

Olivares-Gil,

García-Bustillo

et al. 2024

Healthcare

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Parkinson’s disease (PD) is a progressive neurodegenerative disorder whose prevalence has steadily been rising over the years. Specialist neurologists across the world assess and diagnose patients with PD, although the diagnostic process is time-consuming and various symptoms take years to appear, which means that the diagnosis is prone to human error. The partial automatization of PD assessment and diagnosis through computational processes has therefore been considered for some time. One well-known tool for PD assessment is finger tapping (FT), which can now be assessed through computer vision (CV). Artificial intelligence and related advances over recent decades, more specifically in the area of CV, have made it possible to develop computer systems that can help specialists assess and diagnose PD. The aim of this study is to review some advances related to CV techniques and FT so as to offer insight into future research lines that technological advances are now opening up.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Computer Vision for Parkinson’s Disease Evaluation: A Survey on Finger Tapping

Amo-Salas,

Olivares-Gil,

García-Bustillo

et al. 2024

Healthcare

View full text Add to dashboard Cite

show abstract

“…For example, Parkinson's disease patients usually have motor dysfunction symptoms like tremors, unpaired gait patterns and limb stiffness [18]. The movement frequency of patients is highly likely to be different from that of healthy people, particularly due to tremors, and the use of frequency features may inform deep learning based Parkinson's disease diagnostic systems [19].…”

Section: Impact Overviewmentioning

confidence: 99%

CP-AGCN: Pytorch-based attention informed graph convolutional network for identifying infants at risk of cerebral palsy

Zhang¹,

Ho²,

Shum³

2022

Software Impacts

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Pose-based tremor type and level analysis for Parkinson’s disease from video

Zhang,

Ho,

Zhang

et al. 2024

Int J CARS

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Purpose Current methods for diagnosis of PD rely on clinical examination. The accuracy of diagnosis ranges between 73 and 84%, and is influenced by the experience of the clinical assessor. Hence, an automatic, effective and interpretable supporting system for PD symptom identification would support clinicians in making more robust PD diagnostic decisions. Methods We propose to analyze Parkinson’s tremor (PT) to support the analysis of PD, since PT is one of the most typical symptoms of PD with broad generalizability. To realize the idea, we present SPA-PTA, a deep learning-based PT classification and severity estimation system that takes consumer-grade videos of front-facing humans as input. The core of the system is a novel attention module with a lightweight pyramidal channel-squeezing–fusion architecture that effectively extracts relevant PT information and filters noise. It enhances modeling performance while improving system interpretability. Results We validate our system via individual-based leave-one-out cross-validation on two tasks: the PT classification task and the tremor severity rating estimation task. Our system presents a 91.3% accuracy and 80.0% F1-score in classifying PT with non-PT class, while providing a 76.4% accuracy and 76.7% F1-score in more complex multiclass tremor rating classification task. Conclusion Our system offers a cost-effective PT classification and tremor severity estimation results as warning signs of PD for undiagnosed patients with PT symptoms. In addition, it provides a potential solution for supporting PD diagnosis in regions with limited clinical resources.

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Pose-Based Tremor Classification for Parkinson’s Disease Diagnosis from Video

Cited by 7 publications

References 30 publications

Computer Vision for Parkinson’s Disease Evaluation: A Survey on Finger Tapping

Computer Vision for Parkinson’s Disease Evaluation: A Survey on Finger Tapping

CP-AGCN: Pytorch-based attention informed graph convolutional network for identifying infants at risk of cerebral palsy

Pose-based tremor type and level analysis for Parkinson’s disease from video

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