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：随着可穿戴设备与移动设备日益融入日常生活，它们为持续感知野外人体运动提供了实用途径。但惯性信号高度依赖传感设置，包括身体部位、安装位置、传感器朝向、设备硬件及采样协议。这种设置依赖性使得学习跨设备和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）在封闭式识别场景之外的广泛使用。我们提出AnyMo，一种面向设置无关人体运动建模的几何感知框架。AnyMo利用基于物理的IMU仿真技术，在密集体表位置生成多样且合理的合成信号，通过配对合成放置视角与遮蔽的部分观测预训练图编码器，将多位置IMU信号标记化为全身运动令牌，并利用大语言模型（LLM）对齐这些令牌以实现运动-语言理解。我们在三个互补任务上评估AnyMo：跨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。该框架在人体活动识别（HAR）任务上平均准确率/F1分数/召回率@2分别提升11.7%/11.6%/22.6%，零样本IMU到文本和文本到IMU检索的平均倒数排名（MRR）分别提高15.9%和28.6%，零样本描述生成的BERT-F1提升18.8%。这些结果证实AnyMo可作为野外可穿戴运动理解的通用模型。项目主页：https://baiyuchen.com/project/AnyMo。