Video-Based Hand Pose Estimation for Remote Assessment of Bradykinesia in Parkinson’s Disease
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-46005-0_21
Reference23 articles.
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2. Li, Z., et al.: An automatic evaluation method for Parkinson’s Dyskinesia using finger tapping video for small samples. (2022, in Review). https://doi.org/10.21203/rs.3.rs-1207003/v1
3. Vignoud, G., et al.: Video-based automated assessment of movement parameters consistent with MDS-UPDRS III in Parkinson’s disease. J. Park. Dis. 12(7), 2211–2222 (2022). https://doi.org/10.3233/JPD-223445
4. Yang, N., et al.: Automatic detection pipeline for accessing the motor severity of Parkinson’s disease in finger tapping and postural stability. IEEE Access 10, 66961–66973 (2022). https://doi.org/10.1109/ACCESS.2022.3183232
5. Goetz, C.G., et al.: Movement disorder society-sponsored revision of the unified Parkinson’s disease rating scale (MDS-UPDRS): scale presentation and clinimetric testing results: MDS-UPDRS: clinimetric assessment. Mov. Disord.Disord. 23(15), 2129–2170 (2008). https://doi.org/10.1002/mds.22340
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1. What the trained eye cannot see: Quantitative kinematics and machine learning detect movement deficits in early-stage Parkinson's disease from videos;Parkinsonism & Related Disorders;2024-10
2. Characterizing Disease Progression in Parkinson’s Disease from Videos of the Finger Tapping Test;IEEE Transactions on Neural Systems and Rehabilitation Engineering;2024
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