Automated and accurate human activity recognition (HAR) using body-worn
sensors enables practical and cost efficient remote monitoring of Activity of
DailyLiving (ADL), which are shown to provide clinical insights across multiple
therapeutic areas. Development of accurate algorithms for human activity
recognition(HAR) is hindered by the lack of large real-world labeled datasets.
Furthermore, algorithms seldom work beyond the specific sensor on which they
are prototyped, prompting debate about whether accelerometer-based HAR is even
possible [Tong et al., 2020]. Here we develop a 6-class HAR model with strong
performance when evaluated on real-world datasets not seen during training. Our
model is based on a frozen self-supervised representation learned on a large
unlabeled dataset, combined with a shallow multi-layer perceptron with temporal
smoothing. The model obtains in-dataset state-of-the art performance on the
Capture24 dataset ($\kappa= 0.86$). Out-of-distribution (OOD) performance is
$\kappa = 0.7$, with both the representation and the perceptron models being
trained on data from a different sensor. This work represents a key step
towards device-agnostic HAR models, which can help contribute to increased
standardization of model evaluation in the HAR field.

本研究基于一个大规模无标签数据集上学习的自监督表征及浅层多层感知器模型，在未参与训练的真实世界数据集上取得了良好性能，并可通过不同传感器的数据训练，从而实现了设备无关型人体活动识别模型，将有助于增加 HAR 领域中模型评估的标准化。