{"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/a-generic-multi-modal-dynamic-gesture","title":"A Generic Multi-modal Dynamic Gesture Recognition System using Machine Learning","arxiv_id":"1809.05839","date":"2018-09-16","proceeding":null,"authors":["Gautham Krishna G","Karthik Subramanian Nathan","Yogesh Kumar B","Ankith A Prabhu","Ajay Kannan","Vineeth Vijayaraghavan"],"abstract":"Human computer interaction facilitates intelligent communication between\nhumans and computers, in which gesture recognition plays a prominent role. This\npaper proposes a machine learning system to identify dynamic gestures using\ntri-axial acceleration data acquired from two public datasets. These datasets,\nuWave and Sony, were acquired using accelerometers embedded in Wii remotes and\nsmartwatches, respectively. A dynamic gesture signed by the user is\ncharacterized by a generic set of features extracted across time and frequency\ndomains. The system was analyzed from an end-user perspective and was modelled\nto operate in three modes. The modes of operation determine the subsets of data\nto be used for training and testing the system. From an initial set of seven\nclassifiers, three were chosen to evaluate each dataset across all modes\nrendering the system towards mode-neutrality and dataset-independence. The\nproposed system is able to classify gestures performed at varying speeds with\nminimum preprocessing, making it computationally efficient. Moreover, this\nsystem was found to run on a low-cost embedded platform - Raspberry Pi Zero\n(USD 5), making it economically viable.","url_abs":"http://arxiv.org/abs/1809.05839v1","url_pdf":"http://arxiv.org/pdf/1809.05839v1.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":"a-generic-multi-modal-dynamic-gesture","repo_url":"https://github.com/gauthamkrishna-g/Dynamic-Gesture-Recognition","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"gesture-recognition","task_name":"Gesture Recognition"},{"task_slug":"intelligent-communication","task_name":"Intelligent Communication"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}