Research on Key Technologies of Hand Function Rehabilitation Training Evaluation System Based on Leap Motion

• 作者: Zhiguo Xiao Yifei Zhao Nianfeng Li Shang Zhou Hu Xu
• 来源期刊: 电脑和通信(英文)
• 学科: 工学
• 关键词: Leap Motion IMMERSIVE AHP PCA SVM KNN Hand Function Rehabilitation Evaluation System
• 年，卷（期）: 2021,（1）
• 页码范围: 19-35
• 页数: 17页
• 分类号: TP3

摘要

This paper proposes an immersive training system for patients with hand dysfunction who can perform rehabilitation training independently. The system uses Leap Motion binocular vision sensors to collect human hand information, and uses the improved PCA_{<img src="Edit_d6662636-9073-4fbd-855f-9a36e871d5a4.png" width="10" height="15" alt="" />} (Principal Component Analysis) to perform data fusion on the real-time data collected by the sensor to obtain more hands with fewer principal components, and improve the stability and accuracy of the data. Immediately, the use of improved SVM_{<img src="Edit_10c78725-e09e-4dcf-ae05-e21205df4acc.png" width="10" height="15" alt="" />} (Support Vector Machine) and KNN_{<img src="Edit_0ee97f55-2773-4b48-93b3-93f61aa25577.png" width="10" height="15" alt="" />} (K-Nearest Neighbor Algorithm) for gesture recognition and classification is proposed to enable patients to perform rehabilitation training more effectively. Finally, the effective evaluation results of the rehabilitation effect of patients by the idea of AHP_{<img src="Edit_70dd1964-28be-4137-afa5-9a184704f08e.png" width="10" height="15" alt="" />} (Analytic Hierarchy Process) are taken as necessary reference factors for doctors to follow up treatment. Various experimental results show that the system has achieved the expected results and has a good application prospect.