基于AI的康复游戏系统，能自适应调整难度与内容。
Design Process of a Self Adaptive Smart Serious Games Ecosystem
- 模块化设计整合多模态传感与实时推理
- 支持动态难度调节与程序化内容生成
- 适合临床康复场景，可个性化干预
本文阐述了Blexer v3的设计愿景与演进路径，这是一个基于严肃游戏的模块化、人工智能驱动的康复生态系统。基于前代系统的经验，我们提出了新架构，旨在集成多模态感知、实时推理与智能控制。系统将包含数据采集、用户状态推断与游戏自适应等独立模块。关键功能如动态难度调节（DDA）和程序化内容生成（PCG）也被纳入，以支持个性化干预。本文呈现了Blexer v3的完整概念框架，明确了系统的模块结构与数据流，为下一阶段——开发功能原型并融入临床康复场景——奠定基础。
原文摘要 · Abstract (English)
This paper outlines the design vision and planned evolution of Blexer v3, a modular and AI-driven rehabilitation ecosystem based on serious games. Building on insights from previous versions of the system, we propose a new architecture that aims to integrate multimodal sensing, real-time reasoning, and intelligent control. The envisioned system will include distinct modules for data collection, user state inference, and gameplay adaptation. Key features such as dynamic difficulty adjustment (DDA) and procedural content generation (PCG) are also considered to support personalized interventions. We present the complete conceptual framework of Blexer v3, which defines the modular structure and data flow of the system. This serves as the foundation for the next phase: the development of a functional prototype and its integration into clinical rehabilitation scenarios.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。