HapticSeer: A Multi-Channel, Black-Box, Platform-Agnostic Approach to Detecting Video Game Events for Real-Time Haptic Feedback
Yu-Hsin Lin, Yu-Wei Wang, Pin-Sung Ku, Yun-Ting Cheng, Yuan-Chih Hsu, Ching-Yi Tsai, and
Mike Y. Chen In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Haptic feedback significantly enhances virtual experiences. However, supporting haptics currently requires modifying the codebase, making it impractical to add haptics to popular, high-quality experiences such as best selling games, which are typically closed-source. We present HapticSeer, a multi-channel, black-box, platform-agnostic approach to detecting game events for real-time haptic feedback. The approach is based on two key insights: 1) all games have 3 types of data streams: video, audio, and controller I/O, that can be analyzed in real-time to detect game events, and 2) a small number of user interface design patterns are reused across most games, so that event detectors can be reused effectively. We developed an open-source HapticSeer framework and implemented several real-time event detectors for commercial PC and VR games. We validated system correctness and real-time performance, and discuss feedback from several haptics developers that used the HapticSeer framework to integrate research and commercial haptic devices.
🏆 Honorable Mention Award