Inertia-1 Explores Wearable Motion Foundation Models

Wearable motion sensing offers a continuous and scalable method for monitoring human behavior and health, making it an ideal candidate for foundation models. However, the optimal principles for pretraining and scaling these models remain largely unexplored, with prior research often focusing on isolated design elements under narrow task constraints. To address this gap, researchers have introduced Inertia-1, a comprehensive and open framework dedicated to exploring wearable motion foundation models. This initiative leverages a massive corpus of over 18.2 million hours of accelerometer data from global sources, enabling a controlled study of the entire lifecycle of these models. Inertia-1 systematically investigates critical design choices, including sensor modality, device placement, sampling rate, window length, model architectures, size, pretraining objectives, and data scale. Extensive evaluations across 15 datasets, covering tasks like human activity recognition, freezing-of-gait detection, and disease prediction, have yielded significant findings for building motion foundation models that generalize effectively across various tasks and sensing conditions.