[2203.04153] Easy Ensemble: Simple Deep Ensemble Learning for Sensor-Based Human Activity Recognition Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2203.04153 (cs) [Submitted on 8 Mar 2022 ( v1 ), last revised 25 Apr 2026 (this version, v2)] Title: Easy Ensemble: Simple Deep Ensemble Learning for Sensor-Based Human Activity Recognition Authors: Tatsuhito Hasegawa , Kazuma Kondo View a PDF of the paper titled Easy Ensemble: Simple Deep Ensemble Learning for Sensor-Based Human Activity Recognition, by Tatsuhito Hasegawa and 1 other authors View PDF HTML (experimental) Abstract: Sensor-based human activity recognition (HAR) is a paramount technology in the Internet of Things services. HAR using representation learning, which automatically learns a feature representation from raw data, is the mainstream method because it is difficult to interpret relevant information from raw sensor data to design meaningful features. Ensemble learning is a robust approach to improve generalization performance; however, deep ensemble learning requires various procedures, such as data partitioning and training multiple models, which are time-consuming and computationally expensive. In this study, we propose Easy Ensemble (EE) for HAR, which enables the easy implementation of deep ensemble learning in a single model. In addition, we propose various techniques (input variationer, stepwise ensemble, and channel shuffle) for the EE. Experiments on a benchmark dataset for HAR demonstrated the effectiveness of EE and various techniques and their characteristics compared with conventional ensemble learning methods. Comments: 13 pages, 14 figures, 5 tables. Accepted version. Published in IEEE Internet of Things Journal Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2203.04153 [cs.CV] (or arXiv:2203.04153v2 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2203.04153 Focus to learn more arXiv-issued DOI via DataCite Journal reference: IEEE Internet of Things Journal 10(6):5506-5518 (2023) Related DOI : https://doi.org/10.1109/JIOT.2022.3222221 Focus to learn more DOI(s) linking to related resources Submission history From: Tatsuhito Hasegawa Dr. [ view email ] [v1] Tue, 8 Mar 2022 15:30:32 UTC (414 KB) [v2] Sat, 25 Apr 2026 00:38:05 UTC (734 KB) Full-text links: Access Paper: View a PDF of the paper titled Easy Ensemble: Simple Deep Ensemble Learning for Sensor-Based Human Activity Recognition, by Tatsuhito Hasegawa and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2022-03 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from