CoWear：面向多智能设备的协作式可穿戴惯性定位
CoWear: Towards Collaborative Wearable Inertial Localization with Multiple Smart Devices
AI总结：
CoWear是面向多异构消费级可穿戴设备的协作惯性定位数据集与参考框架，通过角色特定估计器融合多设备预测，提升了定位精度，为该领域提供了可复现基线。
摘要：
消费级智能设备可从不同身体部位观测同一人体运动，但现有惯性定位基准很少提供带设备专属运动真值的同步异构可穿戴设备。本文提出CoWear，这是一个用于协作惯性定位的数据集与参考框架，使用智能手机、智能手表和智能眼镜。该数据集包含30余名参与者、300余次行走会话，累计设备轨迹时长超30小时、总里程超100公里，每个设备均通过动作捕捉单独跟踪。为实现不同身体运动间的协作，我们训练角色特定估计器，在同步时间窗口内预测公共参考设备的位移；其预测通过陀螺仪积分转换至世界坐标系，并根据学习到的不确定性进行融合。以手机、眼镜和手表为参考目标的实验表明，多设备融合相比单设备估计，在位移和轨迹展开精度上均有持续提升。CoWear为研究异构消费级可穿戴设备间的协作惯性定位提供了数据集与可复现基线。
英文摘要：
Consumer smart devices observe the same human motion from different body locations, yet existing inertial-localization benchmarks rarely provide synchronized heterogeneous wearables with device-specific motion ground truth. We present CoWear, a dataset and reference framework for collaborative inertial localization using a smartphone, smartwatch, and smart glasses. The 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 frame using gyroscope integration and fused according to learned uncertainty. Experiments using phone, glasses, and watch as reference targets show that multi-device fusion consistently improves both displacement and trajectory rollout accuracy over individual-device estimation. CoWear provides a dataset and reproducible baseline for studying collaborative inertial localization across heterogeneous consumer wearables.
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