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.

我们通过对多个传感器位置的全面评估，确定了建立位置不变模型的关键身体部位，并开发了可部署的设备内动作模型，实现了 91.41％的帧级 F1 得分。我们还研究了不同位置数据的合成，以减轻数据收集任务的负担，从而推进低门槛、位置不变的活动识别系统的研究。