从触摸日志生成符合生物力学的用户动作，揭示操作背后的肌肉运动机制。
Log2Motion: Biomechanical Motion Synthesis from Touch Logs
- 用强化学习驱动的骨骼肌前向仿真，从触摸日志生成真实动作序列。
- 生成动作包含速度、精度、用力等多维度指标，与真人数据高度一致。
- 适用于人机交互研究、产品可用性评估及触控行为分析。
移动设备的触摸数据虽大规模收集，却难以反映其背后的操作过程。尽管生物力学模拟可揭示运动控制机制，但尚未应用于触控交互。为此，我们提出一项新计算任务：直接从触摸日志合成合理运动。核心思路是利用强化学习驱动的骨骼肌前向仿真，生成与触摸事件一致的生物力学合理动作序列。通过将软件模拟器嵌入物理引擎，使生物力学模型能实时操控真实应用。Log2Motion从触摸日志生成丰富的用户动作，包括运动轨迹、速度、准确性和用力程度。我们通过对比真人动作捕捉数据和已有研究验证生成动作的合理性，并在大规模数据集上展示其有效性。该方法为理解日志数据提供了新视角，揭示触控交互中的人体工程学与运动控制机制。
原文摘要 · Abstract (English)
Touch data from mobile devices are collected at scale but reveal little about the interactions that produce them. While biomechanical simulations can illuminate motor control processes, they have not yet been developed for touch interactions. To close this gap, we propose a novel computational problem: synthesizing plausible motion directly from logs. Our key insight is a reinforcement learning-driven musculoskeletal forward simulation that generates biomechanically plausible motion sequences consistent with events recorded in touch logs. We achieve this by integrating a software emulator into a physics simulator, allowing biomechanical models to manipulate real applications in real-time. Log2Motion produces rich syntheses of user movements from touch logs, including estimates of motion, speed, accuracy, and effort. We assess the plausibility of generated movements by comparing against human data from a motion capture study and prior findings, and demonstrate Log2Motion in a large-scale dataset. Biomechanical motion synthesis provides a new way to understand log data, illuminating the ergonomics and motor control underlying touch interactions.
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