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 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 fram
The proliferation of wearable sensors and the increasing demand for robust human motion understanding in diverse, real-world applications drive the need for setup-agnostic models.
This development allows AI systems to interpret human motion consistently across a wide range of devices and environments, enabling more versatile and reliable applications in health, robotics, and other fields.
Traditional reliance on device-specific or lab-controlled motion sensing is replaced by a more universal approach, expanding the practical utility of inertial measurement units (IMUs).
- · Wearable device manufacturers
- · Robotics companies
- · Health and fitness technology developers
- · AI agents developers
- · Companies relying on proprietary, closed-system motion sensing solutions
- · Developers of highly specialized, non-transferable motion models
Improved human-computer interaction and more natural control of robotic systems become possible through universal motion understanding.
This technology could accelerate the development of sophisticated AI agents that can accurately perceive and respond to human physical cues.
Wider adoption of geometry-aware motion modeling could lead to new standards for IMU data processing, impacting future hardware design and interoperability.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL