A Systematic Study of Unsupervised Domain Adaptation for Robust Human-Activity Recognition
A Systematic Study of Unsupervised Domain Adaptation for Robust Human-Activity Recognition
复制标题
DOI：
10.1145/3380985
复制
发表时间：
2020-03-01
期刊：
影响因子：
--
通讯作者：
Kawsar, Fahim
中科院分区：
文献类型：
--
作者：
Chang, Youngjae;Mathur, Akhil;Kawsar, Fahim
可穿戴传感器正日益成为监测人类活动的主要界面。然而，为了使用可穿戴传感器将人类活动识别（HAR）扩展到数百万用户和设备，HAR计算模型必须对惯性传感器数据中的真实世界异质性具有鲁棒性。在本文中，我们研究了与可穿戴传感器在人体上的放置有关的穿戴多样性问题，并证明即使是最先进的深度学习模型也不能抵抗这些因素。本文的核心贡献在于首次深入研究了无监督域自适应（UDA）算法，在佩戴多样性的背景下，我们在四个HAR数据集上开发和评估了三种自适应技术，以评估它们在解决佩戴多样性问题方面的相对性能。更重要的是，我们还进行了仔细的分析，以了解每个UDA算法的缺点，并揭示了几个隐式的数据相关假设，如果没有这些假设，这些算法的准确性会大幅下降。总而言之，我们的实验结果警告不要将UDA用作使HAR模型适应新领域的银弹，并为HAR从业者提供实用指南，并为未来HAR领域适应研究铺平道路。
Wearable sensors are increasingly becoming the primary interface for monitoring human activities. However, in order to scale human activity recognition (HAR) using wearable sensors to million of users and devices, it is imperative that HAR computational models are robust against real-world heterogeneity in inertial sensor data. In this paper, we study the problem of wearing diversity which pertains to the placement of the wearable sensor on the human body, and demonstrate that even state-of-the-art deep learning models are not robust against these factors. The core contribution of the paper lies in presenting a first-of-its-kind in-depth study of unsupervised domain adaptation (UDA) algorithms in the context of wearing diversity we develop and evaluate three adaptation techniques on four HAR datasets to evaluate their relative performance towards addressing the issue of wearing diversity. More importantly, we also do a careful analysis to learn the downsides of each UDA algorithm and uncover several implicit data-related assumptions without which these algorithms suffer a major degradation in accuracy. Taken together, our experimental findings caution against using UDA as a silver bullet for adapting HAR models to new domains, and serve as practical guidelines for HAR practitioners as well as pave the way for future research on domain adaptation in HAR.