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Sensor Motion Sync Technology: Smoothing vs Instantaneous Latency Tradeoff: Fundamental Mechanics and Deep Mathematical Modeling OFFICIAL BOARDS

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首页 › Forums › Sim Racing & Driving Mechanics › Sensor Motion Sync Technology: Smoothing vs Instantaneous Latency Tradeoff: Fundamental Mechanics and Deep Mathematical Modeling

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  • #119379
    User AvatarCole Torres
    Member

    I’ve been digging into how polling-rate mismatch between the sensor’s motion pipeline and the USB report interval actually introduces variance in input latency, and I want to nail down the real tradeoff between smoothing and instantaneous reporting. When a mouse reports at 1000Hz but the sensor samples internally at a different cadence, you get jitter in the actual delay between a physical movement and its registration on the host. Does the standard 1ms USB frame assumption hold under variable frame delivery, or does interrupt-style polling behave fundamentally differently? And how do you mathematically model that jitter—RMS deviation, or worst-case tail latency?

    For those running custom polling configs, how do you quantify the latency penalty when you prioritize raw immediacy over smoothing? Is there a threshold where predictive interpolation—estimating the next position from current velocity—actually reduces

    "Flat frametimes > peak FPS"
    #126926
    User AvatarEmily Turner
    Member

    Great topic — this is where a lot of people get confused because “motion sync” and “smoothing” aren’t quite the same thing, and the math behind the tradeoff is more intuitive than vendors admit. At the sensor level, your optical sensor samples position at some native rate, but the report you get over USB is a function of both the polling rate and whatever firmware processing sits between the sensor and the host. When a mouse applies smoothing (a moving average or exponential smoothing filter), it’s essentially averaging consecutive samples to kill jitter and high-frequency noise — but every averaging operation is a causal low-pass filter, and the price of that low-pass behavior is a phase delay. Mathematically, for an exponential moving average with smoothing factor α, the output at time n is y[n] = α·x[n] + (1−α)·y[n−1], and the higher α is toward 1, the closer you get to raw data but the more noise you carry; the lower α goes, the smoother the trajectory but the larger the group delay. That delay is

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