A hand movement function analysis system based on machine intelligence

Through the hand motion function analysis system based on machine intelligence, using contactless optical gesture tracking equipment and neural network model, the existing hand rehabilitation evaluation system has been solved, and more efficient and accurate hand motion function evaluation has been achieved.

CN115067934BActive Publication Date: 2025-03-25SHANGHAI UNIV
2 Cites 0 Cited by

Patent Information

Application Number
CN202210682696.5
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-06-16
Publication Date
2025-03-25
Estimated Expiration
2042-06-16

AI Technical Summary

Technical Problem

The existing hand rehabilitation assessment system has problems such as poor application and low evaluation accuracy.

Method used

It provides a hand motion function analysis system based on machine intelligence, including a data acquisition module, a data preprocessing module, a feature set construction module and a motion evaluation module. The system collects hand motion data through contactless optical gesture tracking equipment, performs data preprocessing and feature extraction, and uses neural network models to evaluate motion functions.

Benefits of technology

It improves the accuracy of hand motor function evaluation, makes the evaluation results more objective and reliable, is applicable to hospital, home and community environments, and expands the scope of application.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The present invention relates to a hand movement function analysis system based on machine intelligence, including a data acquisition module for collecting hand movement data of a target patient when performing a preset movement task; a data preprocessing module for extracting movement data of each joint of the hand to supplement missing values ​​and perform data denoising; a feature set construction module for extracting features to obtain hand movement features when performing each type of movement task; and a movement evaluation module for inputting each hand movement feature into a corresponding neural network model, obtaining a score for each type of movement task, and summing the scores for each type of movement task to obtain a total score of the hand movement function of the target patient. The system of the present invention can improve the extensiveness of hand movement evaluation applications and improve the accuracy of evaluation results.
Need to check novelty before this filing date? Find Prior Art