IMUDINO：面向惯性传感的自蒸馏基础模型
IMUDINO: Self-distilled Foundation Model for Inertial Sensing
一篇把 DINO 自监督搬到惯性传感数据的论文，一个模型同时搞定 HAR、动作捕捉、里程计这些任务，还能换采样率和佩戴位置，做可穿戴或机器人方向的可以看看。
arXiv 论文提出 IMUDINO，首个在多类惯性传感任务上取得有竞争力表现的基础模型，覆盖人活动识别（HAR）、健康监测、动作捕捉和里程计。方法上将 DINO 自监督范式适配到惯性数据，通过保留语义的运动变换模拟不同运动模式和传感器配置。架构采用带频率感知位置编码的纯 transformer 编码器，原生支持不同输入长度和采样率，并能泛化到未见过的传感器佩戴位置和机器人本体。此前多数惯性基础模型只覆盖单一子领域，有时还落后于监督式 SOTA 方法。
IMUDINO: Self-distilled Foundation Model for Inertial Sensing
We present IMUDINO, the first foundation model (FM) for inertial sensing to achieve competitive performance across diverse sensing tasks including human activity recognition (HAR), health, motion capture, and odometry. Prior inertial FMs often focus on one subfield of inertial sensing and sometimes underperform state-of-the-art supervised methods (e.g., in HAR), making their scope considerably narrower than FMs for vision and natural language. Moreover, they often require fixed input length and sampling rate, and generalize poorly to unseen sensor locations. To build a general-purpose foundation model for inertial sensing, we adapt the DINO self-supervised learning (SSL) paradigm to inertial data through a set of semantics-preserving motion transformations that simulate variations in motion pattern and sensor setup. For model architecture, we use a plain transformer encoder with frequency-aware positional encoding that natively supports varying input length and sampling rate. This simple setup proved remarkably effective for learning global and dense features generalizable across users, sensor setups, tasks, and, in the case of robot odometry, embodiments. We hope IMUDINO can serve as a backbone for inertial sensing and kickstart the development of general-purpose inertial FMs.