Journal of Jilin University(Engineering and Technology Edition) ›› 2025, Vol. 55 ›› Issue (10): 3274-3282.doi: 10.13229/j.cnki.jdxbgxb.20240066

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Limb movement rehabilitation training evaluation device based on virtual reality technology

Jian ZHAO1,2,3(),Chen YANG1,2,Gui-hai LI1,Li-juan SHI3,4,Zhe-jun KUANG1,3()   

  1. 1.College of Computer Science and Technology，Changchun University，Changchun 130022，China
    2.College of Cyberspace Security，Changchun University，Changchun 130012，China
    3.Jilin Provincial Key Laboratory of Human Health Status Identification Function & Enhancement，Changchun 130022，China
    4.College of Electronic Information Engineering，Changchun University，Changchun 130012，China
  • Received:2024-01-18 Online:2025-10-01 Published:2026-02-03
  • Contact: Zhe-jun KUANG E-mail:zhaojian@ccu.edu.cn;kuangzhejun@ccu.edu.cn

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Abstract:

For patients， the traditional means of physical rehabilitation training have some problems such as high repeatability， lack of individuation and invisible training data. This paper provides the design， implementation and effect evaluation of a physical rehabilitation training evaluation system based on virtual reality technology. The system is designed on the principles of improved patient engagement， personalized rehabilitation， real-time feedback and monitoring， safety and data analysis. This paper conducted experiments on 10 patients. Through virtual simulation environment， patients can participate in more attractive and interactive rehabilitation training， and the system can dynamically adjust rehabilitation programs according to patients' conditions. In addition， the system can also collect a large amount of rehabilitation data， providing detailed information for medical staff to support the optimization and personalized adjustment of the treatment process.

Key words: virtual reality, limb rehabilitation, body recognition, bone coordinates, system evaluation

Fig.1

System framework structure diagram"

Fig.2

Upper and lower limb rehabilitation training evaluation instrument"

Fig.3

Kinect 2.0 technology roadmap"

Fig.4

Interaction between data collection and virtual scenes"

Fig.5

Nine types of specialized training"

Fig.6

Torso flexion training"

Fig.7

Various joint points of human body"

Fig.8

Summary of rehabilitation training visualization data"

Fig.9

Accuracy compared to last time"

Fig.10

Patient data in server backend"

Table 1

Participant information"

参与者ID性别年龄/岁其他病史烧伤程度能否自主训练
1男25否中度可以
2男40否中度可以
3男34否中度可以
4男38否中度可以
5男29否中度可以
6女31否中度可以
7女40否中度可以
8女42否中度可以
9女26否中度可以
10女30否中度可以

Fig.11

Motor standardization before rehabilitation training in control and observation groups"

Fig.12

Movement standardization of control group and observation group after rehabilitation training"

Fig.13

Total boredom score per day"

Fig.14

Total inattention score per day"

Fig.15

An overall score that keeps up with pace every day"

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