Abstract
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 (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 (Support Vector Machine) and KNN (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 (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.
Highlights
According to the data in the report of stroke prevention and treatment in China in 2019, the number of stroke patients over the age of 40 reached 12.42 million
This paper proposes the key technology research of hand function rehabilitation training system based on Leap Motion
By optimizing the traditional fusion algorithms principal component analysis (PCA), KNN, and SVM, Leap Motion is used as a platform to show good results in real time, which greatly helps patients with hand function rehabilitation
Summary
According to the data in the report of stroke prevention and treatment in China in 2019, the number of stroke patients over the age of 40 reached 12.42 million. Stroke rehabilitation has become a major problem for stroke patients [1], so how to use modern human-computer interaction technology to play a certain key to the rehabilitation of patients, compared with large and expensive machinery and equipment, is more important and simpler, and at the same time, it can be afforded by most patients. Under this background, the key technology of hand function rehabilitation evaluation system based on new somatosensory equipment is proposed in Research
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