The ubiquitous availability of smartphones and smartwatches with integrated inertial measurement units (IMUs) enables straightforward capturing of human activities through collecting movement data. For specific applications of sensor based human activity recognition (HAR), however, logistical challenges and burgeoning costs render especially the ground truth annotation of such data a difficult endeavor, resulting in limited scale and diversity of datasets available for deriving effective HAR systems and less than ideal recognition capabilities. Transfer learning, i.e., leveraging publicly available labeled datasets to first learn useful representations that can then be fine-tuned using limited amounts of labeled data from a target domain, can alleviate some of the performance issues of contemporary HAR systems. Yet they can fail when the differences between source and target conditions are too large and / or only few samples from a target application domain are available – each of which are typical challenges in real-world human activity recognition scenarios. In this paper, we present an approach for economic use of publicly available labeled HAR datasets for effective transfer learning. We introduce a novel transfer learning framework–Cross-Domain HAR–which follows the teacher-student self-training paradigm to more effectively recognize activities with very limited label information. It bridges conceptual gaps between source and target domains, including sensor locations and type of activities. Cross-Domain HAR enables substantial performance improvements over the state-of-the-art in sensor-based HAR scenarios. Through our extensive experimental evaluation on a range of benchmark datasets we specifically demonstrate the effectiveness of our approach for practically relevant few shot activity recognition scenarios. We also present a detailed analysis into how the individual components of our framework affect downstream performance and provide practical suggestions for using the framework in real-world applications.

Megha Thukral

Harish Haresamudram

Thomas Plötz

ACM Transactions on Intelligent Systems and Technology

ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 是一本学术期刊，以多学科的视角发表关于智能系统、适用算法和技术的最高质量论文。智能系统是使用人工智能 (AI) 技术提供重要服务（例如，作为更大系统的组件）以允许集成系统在现实世界中感知、推理、学习和智能行动的系统。ACM TIST 是每季出版（一年六期）。每期有 8-11 篇常规论文，大约有 20 个已发表的期刊页面或每篇论文 10,000 字。额外的参考文献、证明、图表或详细的实验结果可以作为单独的附录提交，而过长的论文将被自动拒绝。作者可以在其已发表论文的其他内容中包含仅在线附录，并鼓励与其他读者共享其代码和/或数据。

ACM Transactions on Intelligent Systems and Technology is an academic journal that publishes high-quality papers on intelligent systems, applicable algorithms, and technologies from a multidisciplinary perspective. Intelligent systems use artificial intelligence (AI) technologies to provide essential services, functioning as components of larger systems, enabling integrated systems to perceive, reason, learn, and act intelligently in the real world. ACM TIST is published quarterly, with six issues per year, each containing 8 to 11 regular papers, approximately 20 published journal pages, or up to 10,000 words per paper. Additional references, proofs, charts, or detailed experimental results may be submitted as separate appendices, and excessively long papers will be automatically rejected. Authors are allowed to include online-only appendices in their published papers and are encouraged to share their code and/or data with other readers.

《ACM Transactions on Intelligent Systems and Technology》是一本学术期刊，采用多学科的视角，发表关于智能系统、相关算法和技术的高质量论文。智能系统利用人工智能（AI）技术提供重要服务，作为更大系统的组成部分，使得集成系统能够在现实世界中进行感知、推理、学习和智能行动。该期刊每季度出版六期，每期包含8到11篇常规论文，约20页的已发表期刊内容，或每篇论文不超过10,000字。额外的参考文献、证明、图表或详细的实验结果可以作为附录提交，过长的论文将被自动拒绝。作者可以在已发表论文中包含仅在线附录，并鼓励与其他读者共享其代码和/或数据。

Cross-Domain HAR: Few Shot Transfer Learning for Human Activity Recognition

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Category	Quartile
计算机科学	4
计算机科学, 计算机人工智能	4
计算机科学, 计算机信息系统	4

Cross-Domain HAR: Few Shot Transfer Learning for Human Activity Recognition

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Category	Quartile
COMPUTER SCIENCE, INFORMATION SYSTEMS	1