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 inf...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<sub><img src="Edit_d6662636-9073-4fbd-855f-9a36e871d5a4.png" width="10" height="15" alt="" /></sub> (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<sub><img src="Edit_10c78725-e09e-4dcf-ae05-e21205df4acc.png" width="10" height="15" alt="" /></sub> (Support Vector Machine) and KNN<sub><img src="Edit_0ee97f55-2773-4b48-93b3-93f61aa25577.png" width="10" height="15" alt="" /></sub> (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<sub><img src="Edit_70dd1964-28be-4137-afa5-9a184704f08e.png" width="10" height="15" alt="" /></sub> (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.展开更多
文摘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<sub><img src="Edit_d6662636-9073-4fbd-855f-9a36e871d5a4.png" width="10" height="15" alt="" /></sub> (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<sub><img src="Edit_10c78725-e09e-4dcf-ae05-e21205df4acc.png" width="10" height="15" alt="" /></sub> (Support Vector Machine) and KNN<sub><img src="Edit_0ee97f55-2773-4b48-93b3-93f61aa25577.png" width="10" height="15" alt="" /></sub> (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<sub><img src="Edit_70dd1964-28be-4137-afa5-9a184704f08e.png" width="10" height="15" alt="" /></sub> (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.
