研究人员开发了AnyMo，一个新颖的框架，旨在克服从可穿戴惯性测量单元（IMU）建模人体运动中的设置依赖性挑战。该系统利用基于物理的模拟来生成合成数据，使图编码器能够学习对传感器放置和设备变化不敏感的表示。AnyMo将多位置IMU数据进行分词，并将其与大型语言模型对齐以增强运动理解，在零样本活动识别、跨模态检索和运动描述方面取得了显著改进。 AI
影响 能够从可穿戴传感器中进行更鲁棒和可迁移的人体运动分析，可能改进医疗保健、体育和机器人领域的应用。
排序理由 该集群包含一篇详细介绍人体运动建模新框架的学术论文。
在 Hugging Face Daily Papers 阅读 →
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arXiv:2605.22715v1 Announce Type: cross Abstract: 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 l…
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, de…
AnyMo is a geometry-aware framework that enables setup-agnostic human motion modeling using physics-grounded IMU simulation and graph encoding for cross-dataset activity recognition and cross-modal retrieval.