{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/unsupervised-deep-learning-based-clustering","title":"Unsupervised Deep Learning-based clustering for Human Activity Recognition","arxiv_id":"2211.05483","date":"2022-11-10","proceeding":null,"authors":["Hamza Amrani","Daniela Micucci","Paolo Napoletano"],"abstract":"One of the main problems in applying deep learning techniques to recognize activities of daily living (ADLs) based on inertial sensors is the lack of appropriately large labelled datasets to train deep learning-based models. A large amount of data would be available due to the wide spread of mobile devices equipped with inertial sensors that can collect data to recognize human activities. Unfortunately, this data is not labelled. The paper proposes DISC (Deep Inertial Sensory Clustering), a DL-based clustering architecture that automatically labels multi-dimensional inertial signals. In particular, the architecture combines a recurrent AutoEncoder and a clustering criterion to predict unlabelled human activities-related signals. The proposed architecture is evaluated on three publicly available HAR datasets and compared with four well-known end-to-end deep clustering approaches. The experiments demonstrate the effectiveness of DISC on both clustering accuracy and normalized mutual information metrics.","url_abs":"https://arxiv.org/abs/2211.05483v1","url_pdf":"https://arxiv.org/pdf/2211.05483v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"unsupervised-deep-learning-based-clustering","repo_url":"https://github.com/hamzaamrani/Deep-Inertial-Sensory-Clustering","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"activity-recognition","task_name":"Activity Recognition"},{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"deep-clustering","task_name":"Deep Clustering"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"human-activity-recognition","task_name":"Human Activity Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}