TFTune: Creation and Personalization of Pointing Transfer Functions Using Reinforcement Learning

Pointing transfer functions define the mapping between input devices and onscreen cursor movement. Despite being used by millions daily, only marginal improvements in pointing performance have been achieved by tuning transfer functions since the introduction of acceleration-based gains. We present TFTune, a reinforcement learning-based approach for improving pointing by automatically tuning personalized transfer functions. We show that TFTune-generated functions outperform operating system defaults, improving movement times by 7% on macOS when using a trackpad (7 minutes of tuning) and 8% on participants’ personal Windows computers with hardware (i.e. mice and monitors) of varying characteristics (after just 1 minute of tuning). Further, we show that TFTune generalizes beyond traditional pointing devices, providing 16% improvement for a muscle-computer interface (2 minutes of tuning). TFTune demonstrates an initial approach for scalable and meaningful performance improvements in input–output mappings, opening a new direction for exploring the use of machine learning for improving fundamental computer inputs.