Real‐Time Adaptation: Integrating Gesture, Voice, and Dynamic Difficulty in Classic Light‐Gun Games

Applying modern human–computer interaction (HCI) principles to legacy systems poses a unique challenge. Classic light‐gun games exemplify this through two core limitations: dependence on obsolete, proprietary hardware and a static, nonadaptive difficulty curve. The presented multimodal adaptive integration (MAI) approach addresses this by introducing a novel, noninvasive methodology. This approach integrates a vision‐based multimodal input system that utilizes gesture recognition to translate hand and voice commands into mouse input. Concurrently, an external Dynamic Difficulty Adjustment (DDA) engine operates within the emulator to modulate game speed in real‐time based on live performance metrics. A user study ( N = 41) across three classic titles validated the system. Results showed the adaptive mode enhanced objective metrics, with mean increases of 19.8% in playtime, 25.1% in stage progression, and a 41.3% reduction in failures compared with the baseline. Subjectively, 69.8% of participants preferred the adaptive experience. The findings demonstrate that the MAI approach successfully retrofits gesture recognition and DDA into closed‐system games, enabling more accessible and personalized interaction with digital heritage.