We propose a text-to-IMU (inertial measurement unit) motion-synthesis framework to obtain realistic IMU data by fine-tuning a pretrained diffusion model with an acceleration-based second-order loss (L_acc). L_acc enforces consistency in the discrete second-order temporal differences of the generated motion, thereby aligning the diffusion prior with IMU-specific acceleration patterns. We integrate L_acc into the training objective of an existing diffusion model, finetune the model to obtain an IMU-specific motion prior, and evaluate the model with an existing text-to-IMU framework that comprises surface modelling and virtual sensor simulation. We analysed acceleration signal fidelity and differences between synthetic motion representation and actual IMU recordings. As a downstream application, we evaluated Human Activity Recognition (HAR) and compared the classification performance using data of our method with the earlier diffusion model and two additional diffusion model baselines. When we augmented the earlier diffusion model objective with L_acc and continued training, L_acc decreased by 12.7% relative to the original model. The improvements were considerably larger in high-dynamic activities (i.e., running, jumping) compared to low-dynamic activities~(i.e., sitting, standing). In a low-dimensional embedding, the synthetic IMU data produced by our refined model shifts closer to the distribution of real IMU recordings. HAR classification trained exclusively on our refined synthetic IMU data improved performance by 8.7% compared to the earlier diffusion model and by 7.6% over the best-performing comparison diffusion model. We conclude that acceleration-aware diffusion refinement provides an effective approach to align motion generation and IMU synthesis and highlights how flexible deep learning pipelines are for specialising generic text-to-motion priors to sensor-specific tasks.

翻译：我们提出了一种文本到IMU（惯性测量单元）的运动合成框架，通过使用基于加速度的二阶损失函数（L_acc）微调预训练的扩散模型，以获得逼真的IMU数据。L_acc强制生成运动在离散二阶时间差分上的一致性，从而使扩散先验与IMU特有的加速度模式对齐。我们将L_acc集成到现有扩散模型的训练目标中，微调模型以获得IMU特定的运动先验，并使用一个包含表面建模和虚拟传感器模拟的现有文本到IMU框架评估模型。我们分析了加速度信号的保真度以及合成运动表示与实际IMU记录之间的差异。作为下游应用，我们评估了人类活动识别（HAR），并比较了使用我们方法的数据与早期扩散模型及两个额外扩散模型基线的分类性能。当我们在早期扩散模型目标中加入L_acc并继续训练时，L_acc相对于原始模型降低了12.7%。在高动态活动（如跑步、跳跃）中，改进幅度显著大于低动态活动（如坐、站）。在低维嵌入中，我们优化模型生成的合成IMU数据更接近真实IMU记录的分布。仅使用我们优化的合成IMU数据训练的HAR分类性能，相比早期扩散模型提高了8.7%，相比性能最佳的对比扩散模型提高了7.6%。我们得出结论：加速度感知的扩散优化提供了一种有效的方法来对齐运动生成和IMU合成，并突显了深度学习流程在将通用文本到运动先验专门化用于传感器特定任务方面的灵活性。