{"id":1172,"date":"2023-03-24T11:53:40","date_gmt":"2023-03-24T10:53:40","guid":{"rendered":"https:\/\/cbpr.center\/?post_type=research-library&#038;p=1172"},"modified":"2023-12-07T16:35:59","modified_gmt":"2023-12-07T15:35:59","slug":"real-time-classification-of-non-weight-bearing-lower-limb-movements-using-emg-to-facilitate-phantom-motor-execution-engineering-and-case-study-application-on-phantom-limb-pain","status":"publish","type":"research-library","link":"https:\/\/cbpr.center\/sv\/research-library\/real-time-classification-of-non-weight-bearing-lower-limb-movements-using-emg-to-facilitate-phantom-motor-execution-engineering-and-case-study-application-on-phantom-limb-pain\/","title":{"rendered":"Real-time Classification of Non-Weight Bearing Lower-Limb Movements Using EMG to Facilitate Phantom Motor Execution: Engineering and Case Study Application on Phantom Limb Pain"},"content":{"rendered":"When measuring electric signals from the muscles of the leg to treat phantom limb pain, the placement of the electrodes which measures the signal is important, but it is not known how to place the electrodes for getting the best signals for analysing the electric signals with artificial intelligence algorithms. We tested different electrodes placements, such as putting two electrodes on a specific muscle compared to putting one electrode on several untargeted muscles. The best placement was putting one electrode on several untargeted muscles and we presented a case study who had a 68% reduction in pain after 23 sessions. Removing the need to target the muscles means that electrode placement will be easier which can make it easier to perform phantom limb pain treatments measuring electric signals from the muscles of the leg.","protected":false},"parent":0,"template":"","class_list":["post-1172","research-library","type-research-library","status-publish","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v19.14 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Real-time Classification of Non-Weight Bearing Lower-Limb Movements Using EMG to Facilitate Phantom Motor Execution: Engineering and Case Study Application on Phantom Limb Pain - Center for Bionics and Pain Research<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/cbpr.center\/sv\/research-library\/real-time-classification-of-non-weight-bearing-lower-limb-movements-using-emg-to-facilitate-phantom-motor-execution-engineering-and-case-study-application-on-phantom-limb-pain\/\" \/>\n<meta property=\"og:locale\" content=\"sv_SE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Real-time Classification of Non-Weight Bearing Lower-Limb Movements Using EMG to Facilitate Phantom Motor Execution: Engineering and Case Study Application on Phantom Limb Pain - Center for Bionics and Pain Research\" \/>\n<meta property=\"og:description\" content=\"When measuring electric signals from the muscles of the leg to treat phantom limb pain, the placement of the electrodes which measures the signal is important, but it is not known how to place the electrodes for getting the best signals for analysing the electric signals with artificial intelligence algorithms. 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