Tag
This paper introduces Dynamic Influence Weighting (DIW), a knowledge distillation method that improves single-IMU activity recognition by dynamically weighting teacher targets from multiple IMUs during training, achieving significant performance gains.
This paper presents an equipment-centric framework that uses deep learning-based vision and event-driven finite state machines to localize workpieces in hot forging factories, achieving high detection accuracy and low latency in operational settings.
Presents SERUM, a multi-pass framework that extracts structured behavioral models of user actions and intents from raw egocentric video using hierarchical VLM annotation, reducing hallucinations and producing interpretable process models without manual annotation.
Inertia-1 is a research project that systematically explores the full lifecycle of motion models—data, sensing, objectives, and scale—to produce a unified representation that transfers across body placements, devices, and tasks without retraining, leveraging self-supervised pretraining on 18 million hours of accelerometry data.
AnyMo is a geometry-aware framework for setup-agnostic human motion modeling using physics-grounded IMU simulation and graph encoding, achieving significant improvements in zero-shot activity recognition, cross-modal retrieval, and motion captioning across multiple datasets.