{"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/development-of-a-hand-pose-recognition-system-1","title":"Development of a hand pose recognition system on an embedded computer using Artificial Intelligence","arxiv_id":null,"date":"2019-10-03","proceeding":"IEEE International Conference on Electronics, Electrical Engineering and Computing (INTERCON) 2019 10","authors":["Dennis Núñez-Fernández"],"abstract":"The recognition of hand gestures is a very interesting research topic due to the growing demand in recent years in robotics, virtual reality, autonomous driving systems, human-machine interfaces and in other new technologies. Despite several approaches for a robust recognition system, gesture recognition based on visual perception has many advantages over devices such as sensors, or electronic gloves. This paper describes the implementation of a visual-based recognition system on a embedded computer for 10 hand poses recognition. Hand detection is achieved using a tracking algorithm and classification by a light convolutional neural network. Results show an accuracy of 94.50%, a low power consumption and a near real-time response. Thereby, the proposed system could be applied in a large range of applications, from robotics to entertainment.","url_abs":"https://ieeexplore.ieee.org/document/8853573","url_pdf":"https://dennishnf.bitbucket.io/research/2019%20-%20intercon%202019%20-%20hand%20pose%20on%20embedded%20computer%20using%20ai.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":"development-of-a-hand-pose-recognition-system-1","repo_url":"https://github.com/dennishnf/cnn-hand-gesture-interface","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"gesture-recognition","task_name":"Gesture Recognition"},{"task_slug":"hand-detection","task_name":"Hand Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}