{"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/near-real-time-data-labeling-using-a-depth","title":"Near Real-Time Data Labeling Using a Depth Sensor for EMG Based Prosthetic Arms","arxiv_id":"1811.04239","date":"2018-11-10","proceeding":null,"authors":["Geesara Prathap","Titus Nanda Kumara","Roshan Ragel"],"abstract":"Recognizing sEMG (Surface Electromyography) signals belonging to a particular\naction (e.g., lateral arm raise) automatically is a challenging task as EMG\nsignals themselves have a lot of variation even for the same action due to\nseveral factors. To overcome this issue, there should be a proper separation\nwhich indicates similar patterns repetitively for a particular action in raw\nsignals. A repetitive pattern is not always matched because the same action can\nbe carried out with different time duration. Thus, a depth sensor (Kinect) was\nused for pattern identification where three joint angles were recording\ncontinuously which is clearly separable for a particular action while recording\nsEMG signals. To Segment out a repetitive pattern in angle data, MDTW (Moving\nDynamic Time Warping) approach is introduced. This technique is allowed to\nretrieve suspected motion of interest from raw signals. MDTW based on DTW\nalgorithm, but it will be moving through the whole dataset in a pre-defined\nmanner which is capable of picking up almost all the suspected segments inside\na given dataset an optimal way. Elevated bicep curl and lateral arm raise\nmovements are taken as motions of interest to show how the proposed technique\ncan be employed to achieve auto identification and labelling. The full\nimplementation is available at https://github.com/GPrathap/OpenBCIPython","url_abs":"http://arxiv.org/abs/1811.04239v1","url_pdf":"http://arxiv.org/pdf/1811.04239v1.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":"near-real-time-data-labeling-using-a-depth","repo_url":"https://github.com/GPrathap/OpenBCIPython","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"dynamic-time-warping","task_name":"Dynamic Time Warping"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}