Objective Assessment of the Finger Tapping Task in Parkinson's Disease and Control Subjects using Azure Kinect and Machine Learning
Author:
Affiliation:
1. Politecnico di Torino,Turin,Italy
2. CNR-IEIIT Italy,Turin,Italy
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10178426/10178704/10178872.pdf?arnumber=10178872
Reference25 articles.
1. Using Smartphones and Machine Learning to Quantify Parkinson Disease Severity
2. Remote smartphone monitoring of Parkinson’s disease and individual response to therapy
3. GMH-D: Combining Google MediaPipe and RGB-Depth Cameras for Hand Motor Skills Remote Assessment
4. A Mobile Application for Smart Computer-Aided Self-Administered Testing of Cognition, Speech, and Motor Impairment
5. An Expert System for Quantification of Bradykinesia Based on Wearable Inertial Sensors
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Hand tracking for clinical applications: Validation of the Google MediaPipe Hand (GMH) and the depth-enhanced GMH-D frameworks;Biomedical Signal Processing and Control;2024-10
2. Computer Vision for Parkinson’s Disease Evaluation: A Survey on Finger Tapping;Healthcare;2024-02-08
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