As wearable and mobile devices become increasingly embedded in daily life, they offer a practical way to continuously sense human motion in the wild. But inertial signals are highly dependent on the sensing setup, including body location, mounting position, sensor orientation, device hardware, and sampling protocol. This setup dependence makes it difficult to learn motion representations that transfer across devices and datasets, and limits the broader use of wearable IMUs beyond closed-set recognition. We introduce AnyMo, a geometry-aware framework for setup-agnostic human motion modeling. AnyMo uses physics-grounded IMU simulation over dense body-surface placements to generate diverse and plausible synthetic signals, pre-trains a graph encoder from paired synthetic placement views and masked partial observations, tokenizes multi-position IMU into full-body motion tokens, and aligns these tokens with an LLM for motion-language understanding. We evaluate AnyMo on three complementary tasks: zero-shot activity recognition across 14 unseen downstream datasets, cross-modal retrieval, and wearable IMU motion captioning, where it improves average Accuracy/F1/R@2 by 11.7\%/11.6\%/22.6\% on HAR, increases zero-shot IMU-to-text and text-to-IMU retrieval MRR by 15.9\% and 28.6\%, respectively, and improves zero-shot captioning BERT-F1 by 18.8\%. These results support AnyMo as a generalist model for wearable motion understanding in the wild. Project page: https://baiyuchen.com/project/AnyMo.

翻译：随着可穿戴与移动设备日益融入日常生活，它们为在真实场景中持续感知人体运动提供了实用途径。然而，惯性信号高度依赖于传感设置，包括身体位置、佩戴方位、传感器朝向、设备硬件及采样协议等。这种设置依赖性使得学习跨设备与数据集迁移的运动表征变得困难，并限制了可穿戴惯性测量单元（IMU）在封闭集识别任务之外的广泛使用。我们提出AnyMo——一个用于设置无关人体运动建模的几何感知框架。AnyMo通过在密集体表位置进行基于物理的IMU仿真，生成多样且合理的合成信号；利用配对合成视图与掩蔽部分观测预训练图编码器；将多位置IMU信号分词化为全身运动标记；并将这些标记与大语言模型（LLM）对齐以进行运动-语言理解。我们在三个互补任务上评估AnyMo：跨越14个未见下游数据集的零样本活动识别、跨模态检索以及可穿戴IMU运动描述生成。结果显示，在人体活动识别（HAR）任务上，平均准确率/F1分数/R@2分别提升11.7%/11.6%/22.6%；零样本IMU到文本与文本到IMU检索的平均倒数排名（MRR）分别提升15.9%与28.6%；零样本描述生成的BERT-F1提升18.8%。这些结果验证了AnyMo作为真实场景可穿戴运动理解通用模型的有效性。项目页面：https://baiyuchen.com/project/AnyMo。