CoWear: Collaborative Inertial Localization with Multi-Device Wearables
Overview
CoWear presents a dataset and framework for collaborative inertial localization using multiple wearables, improving accuracy through multi-device fusion compared to single-device estimates. (2026-10-15).
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Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · participants: 30 other · Basis not reported
“dataset contains over 30 participants, over 300 walking sessions, and more than 30 hours and 100 km of aggregate device trajectories, with each device individually tracked by motion capture. To enable collaboration across distinct body motions, we train role-specific estimators to predict the displacement of a common reference device over synchronized temporal windows. Their predictions are transformed to the world f”
Exact source · revision 1Source owner not reported
Reported quantity · walking sessions: 300 other · Basis not reported
“dataset contains over 30 participants, over 300 walking sessions, and more than 30 hours and 100 km of aggregate device trajectories, with each device individually tracked by motion capture. To enable collaboration across distinct body motions, we train role-specific estimators to predict the displacement of a common reference device over synchronized temporal windows. Their predictions are transformed to the world f”
Exact source · revision 1Source owner not reported
Reported quantity · aggregate device trajectories: 30 hours · Basis not reported · Differing source assertions
“dataset contains over 30 participants, over 300 walking sessions, and more than 30 hours and 100 km of aggregate device trajectories, with each device individually tracked by motion capture. To enable collaboration across distinct body motions, we train role-specific estimators to predict the displacement of a common reference device over synchronized temporal windows. Their predictions are transformed to the world f”
Exact source · revision 1Source owner not reported
Reported quantity · aggregate device trajectories: 100 other · Basis not reported · Differing source assertions
“dataset contains over 30 participants, over 300 walking sessions, and more than 30 hours and 100 km of aggregate device trajectories, with each device individually tracked by motion capture. To enable collaboration across distinct body motions, we train role-specific estimators to predict the displacement of a common reference device over synchronized temporal windows. Their predictions are transformed to the world f”
Exact source · revision 1Source owner not reported
Reported quantity · distance: 100 other · Basis not reported
“dataset contains over 30 participants, over 300 walking sessions, and more than 30 hours and 100 km of aggregate device trajectories, with each device individually tracked by motion capture. To enable collaboration across distinct body motions, we train role-specific estimators to predict the displacement of a common reference device over synchronized temporal windows. Their predictions are transformed to the world f”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsCoWear: Collaborative Inertial Localization with Multi-Device Wearables
CoWear introduces a dataset and framework for collaborative inertial localization using smartphones, smartwatches, and smart glasses. Multi-device fusion improves displacement and trajectory accuracy compared to individual device estimates.
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