Wearable sensors have permeated into people's lives, ushering impactful applications in interactive systems and activity recognition. However, practitioners face significant obstacles when dealing with sensing heterogeneities, requiring custom models for different platforms. In this paper, we conduct a comprehensive evaluation of the generalizability of motion models across sensor locations. Our analysis highlights this challenge and identifies key on-body locations for building location-invariant models that can be integrated on any device. For this, we introduce the largest multi-location activity dataset (N=50, 200 cumulative hours), which we make publicly available. We also present deployable on-device motion models reaching 91.41% frame-level F1-score from a single model irrespective of sensor placements. Lastly, we investigate cross-location data synthesis, aiming to alleviate the laborious data collection tasks by synthesizing data in one location given data from another. These contributions advance our vision of low-barrier, location-invariant activity recognition systems, catalyzing research in HCI and ubiquitous computing.

翻译：可穿戴传感器已渗透到人们的日常生活中，推动了交互系统和活动识别领域的重大应用。然而，实践者在应对传感异质性时面临重大障碍，需要为不同平台定制模型。本文对运动模型在传感器位置间的泛化能力进行了全面评估。我们的分析突显了这一挑战，并确定了构建可集成于任何设备的位置无关模型的关键人体佩戴位置。为此，我们引入了最大的多位置活动数据集（N=50，累计200小时），并将其公开提供。我们还提出了可部署的设备端运动模型，无论传感器放置位置如何，该模型可达到91.41%的帧级F1分数。最后，我们研究了跨位置数据合成技术，旨在通过利用某一位置的数据合成另一位置的数据来减轻繁琐的数据采集任务。这些贡献推进了我们构建低门槛、位置无关活动识别系统的愿景，促进了人机交互与普适计算领域的研究。