T. Dewantoro, R. Sigit, Heny Yuniarti, Yudith Dian Prawitri, Fridastya Andini Pamudyaningrum, M. I. Awal
{"title":"Myo臂环在脑卒中后手部康复移动应用中的实现","authors":"T. Dewantoro, R. Sigit, Heny Yuniarti, Yudith Dian Prawitri, Fridastya Andini Pamudyaningrum, M. I. Awal","doi":"10.11591/eecsi.v5.1585","DOIUrl":null,"url":null,"abstract":"Medical rehabilitation is one of the efforts to restore motor function of post-stroke patients, but the biggest factor that makes patients quickly restore motor function by active patient movement exercises. The movement in question is the movement carried out every day outside medical rehabilitation at the hospital. On the other hand, patients are reluctant to do therapy independently outside the hospital, because there is no tool that supports patients to do so. So, we need a device that helps patients to do therapy independently. The device is connected to Myo Armband to read the gestures of the patient by looking at the EMG signal from the patient's hand. Then the system performs matching gestures during therapy with EMG signal data that has been trained. The motion matching is done by calculating the Euclidean distance between the two EMG signal data obtained from the Myo Armband device. From the results of the tests carried out, the accuracy of movement matching results obtained an average accuracy of 89.67 percent for flexion-extension gestures and 82 percent for pronation-supination gestures. It can be concluded that Myo Armband in the Mobile Application can be used for Rehabilitation of post stroke patient hands.","PeriodicalId":20498,"journal":{"name":"Proceeding of the Electrical Engineering Computer Science and Informatics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Implementation of Myo Armband on Mobile Application for Post-stroke Patient Hand Rehabilitation\",\"authors\":\"T. Dewantoro, R. Sigit, Heny Yuniarti, Yudith Dian Prawitri, Fridastya Andini Pamudyaningrum, M. I. Awal\",\"doi\":\"10.11591/eecsi.v5.1585\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Medical rehabilitation is one of the efforts to restore motor function of post-stroke patients, but the biggest factor that makes patients quickly restore motor function by active patient movement exercises. The movement in question is the movement carried out every day outside medical rehabilitation at the hospital. On the other hand, patients are reluctant to do therapy independently outside the hospital, because there is no tool that supports patients to do so. So, we need a device that helps patients to do therapy independently. The device is connected to Myo Armband to read the gestures of the patient by looking at the EMG signal from the patient's hand. Then the system performs matching gestures during therapy with EMG signal data that has been trained. The motion matching is done by calculating the Euclidean distance between the two EMG signal data obtained from the Myo Armband device. From the results of the tests carried out, the accuracy of movement matching results obtained an average accuracy of 89.67 percent for flexion-extension gestures and 82 percent for pronation-supination gestures. It can be concluded that Myo Armband in the Mobile Application can be used for Rehabilitation of post stroke patient hands.\",\"PeriodicalId\":20498,\"journal\":{\"name\":\"Proceeding of the Electrical Engineering Computer Science and Informatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceeding of the Electrical Engineering Computer Science and Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.11591/eecsi.v5.1585\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceeding of the Electrical Engineering Computer Science and Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.11591/eecsi.v5.1585","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Implementation of Myo Armband on Mobile Application for Post-stroke Patient Hand Rehabilitation
Medical rehabilitation is one of the efforts to restore motor function of post-stroke patients, but the biggest factor that makes patients quickly restore motor function by active patient movement exercises. The movement in question is the movement carried out every day outside medical rehabilitation at the hospital. On the other hand, patients are reluctant to do therapy independently outside the hospital, because there is no tool that supports patients to do so. So, we need a device that helps patients to do therapy independently. The device is connected to Myo Armband to read the gestures of the patient by looking at the EMG signal from the patient's hand. Then the system performs matching gestures during therapy with EMG signal data that has been trained. The motion matching is done by calculating the Euclidean distance between the two EMG signal data obtained from the Myo Armband device. From the results of the tests carried out, the accuracy of movement matching results obtained an average accuracy of 89.67 percent for flexion-extension gestures and 82 percent for pronation-supination gestures. It can be concluded that Myo Armband in the Mobile Application can be used for Rehabilitation of post stroke patient hands.