Study on the interaction of an AI-based motion capture technology in rehabilitation training systems for neuromyelitis optica

doi:10.11996/JG.j.2095-302X.2025040783

Abstract

Abstract:

Artificial intelligence (AI) vision models and their motion capture technologies have attracted much attention in the medical field in recent years, but these technologies have not been extensively applied to the field of neuromyelitis optica (NMO) rehabilitation. The purpose of this study was to study and implement a rehabilitation training system for neuromyelitis optica through Mediapipe posture tracking technology and interactive feedback design. Specifically, the following core works were conducted: ① Research on the formulation of rehabilitation training plans. Through a combination of qualitative and quantitative methods, 36 NMO rehabilitation patients were shadowed and interviewed, and the follow-up form of the Chinese Central Nervous System Inflammatory Demyelinating Disease Registry was used to evaluate the comprehensive evaluation system. ② Feedback analysis of rehabilitation training action guidance. The rehabilitation training action library was designed, and the key indicators such as the coordinates of human joint points, the angle of joint point formation, and the difference between joint point information and standard information were determined. The human posture detection model was also constructed using the Mediapipe framework to realize the real-time recognition and feedback of rehabilitation training actions. ③ Design of interactive feedback mechanism of rehabilitation system. The design principles of timeliness, particularity, guidance, and traceability were established. Meanwhile, a feedback process that includes scene positioning, dimensional analysis, and interaction mode was designed. Additionally, the Fogg behavior model theory was introduced..Finally, a lightweight rehabilitation training platform based on WeChat applets was built. In this study, a set of neuromyelitis optica rehabilitation training system and its supporting methods were obtained, which verified the possibility of AI motion capture technology in this field, improved the experience of NMO patients in the process of rehabilitation training, and was actually promoted in Beijing Tiantan Hospital affiliated to Capital Medical University, demonstrating its wide range of application value.

Key words: neuromyelitis optica, rehabilitation training, motion recognition, computer vision, multi-sensory interaction

CLC Number:

ZHANG Shuai, HONG Ao, HU Hengrui, LAN Mingying, XI Xiaochao. Study on the interaction of an AI-based motion capture technology in rehabilitation training systems for neuromyelitis optica[J]. Journal of Graphics, 2025, 46(4): 783-792.

Figures/Tables 13

Fig. 1 System architecture

Table 1 Comparison of results of MoCA, SDMT, PASAT scale between case group and healthy group

分组	例数	MoCA	SDMT	PASAT
病例组	36	24.7±4.2	31.1±13.0	48.1±10.3
健康组	40	27.5±2.9	45.0±7.1	50.3±9.7

Table 2 Examples of specific data for the type of motion

Fig. 2 Examples of the 4 types of exercises ((a) Shoulder wrap; (b) Chest expansion; (c) Trunk extension; (d) Lateral flexion)

Fig. 3 Schematic diagram of key nodes in the model

Fig. 4 Schematic diagram of the angular smoothing effect ((a) Wrist joint smooth front; (b) After smoothing the wrist joint; (c) Elbow joint smooth front; (d) After smoothing the elbow joint)

Fig. 5 Scene positioning and dimensional analysis

Fig. 6 Fogg behavioral model

Fig. 7 Schematic diagram of the system interaction feedback mode

Fig. 8 Mini Program system architecture diagram

Fig. 9 Diagram of the user interface of the applet

Fig. 10 User test diagram

Table 3 Rehabilitation training evaluation form

References 14

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