# Design Process of a Self Adaptive Smart Serious Games Ecosystem

arXiv: https://arxiv.org/abs/2510.04615 · HTML: https://mlsanity.com/paper/2510.04615/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 6 Oct 2025.

## Builds on (most cited first, 4 of 4)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Dynamic Difficulty Adjustment in Virtual Reality Exergames through Experience-driven Procedural Content Generation](https://mlsanity.com/paper/2108.08762/) (2021)
- [Visualization in virtual reality: a systematic review](https://mlsanity.com/paper/2203.07616/) (2022)
- [Continuous Reinforcement Learning-based Dynamic Difficulty Adjustment in a Visual Working Memory Game](https://mlsanity.com/paper/2308.12726/) (2023)

## Abstract (arXiv)

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.