{"title":"基于手部运动传感器的自我肢体康复系统","authors":"S. M. S. Nugroho, M. Fauzan, I. Purnama","doi":"10.1109/CENIM48368.2019.8973365","DOIUrl":null,"url":null,"abstract":"Stroke is one of the diseases that cause high number of disability and mortality in Indonesia. The number of deaths from stroke in Indonesia reached 15.4% in almost all hospitals in Indonesia. One of the steps to help stroke patients to improve their motor function, speech, cognition, and other impaired functions is to conduct a series of post-stroke rehabilitation. Post-stroke rehabilitation can be done with direct supervision of the physiotherapist or performed alone at home or commonly called self-rehabilitation. Post-stroke rehabilitation has a variety of movements training, one of which is the movement of a finger. MedCap emerged as one of the tools that can help physiotherapists and patients to rehabilitate post-stroke fingers movement. MedCap which uses Leap Motion hand motion sensor can record a predetermined reference movement with a success rate of 62.35%. MedCap can also calculate the conformity of the reference movement and the real time movement of the patient using the Euclidean Distance method as a form of feedback to the physiotherapist and patient. The method used by MedCap can calculate the conformity of movement with real time movement with an average value of 43.2495%.","PeriodicalId":106778,"journal":{"name":"2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)","volume":"47 1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Self-Physical Rehabilitation System based on Hand Motion Sensor\",\"authors\":\"S. M. S. Nugroho, M. Fauzan, I. Purnama\",\"doi\":\"10.1109/CENIM48368.2019.8973365\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Stroke is one of the diseases that cause high number of disability and mortality in Indonesia. The number of deaths from stroke in Indonesia reached 15.4% in almost all hospitals in Indonesia. One of the steps to help stroke patients to improve their motor function, speech, cognition, and other impaired functions is to conduct a series of post-stroke rehabilitation. Post-stroke rehabilitation can be done with direct supervision of the physiotherapist or performed alone at home or commonly called self-rehabilitation. Post-stroke rehabilitation has a variety of movements training, one of which is the movement of a finger. MedCap emerged as one of the tools that can help physiotherapists and patients to rehabilitate post-stroke fingers movement. MedCap which uses Leap Motion hand motion sensor can record a predetermined reference movement with a success rate of 62.35%. MedCap can also calculate the conformity of the reference movement and the real time movement of the patient using the Euclidean Distance method as a form of feedback to the physiotherapist and patient. The method used by MedCap can calculate the conformity of movement with real time movement with an average value of 43.2495%.\",\"PeriodicalId\":106778,\"journal\":{\"name\":\"2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)\",\"volume\":\"47 1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CENIM48368.2019.8973365\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CENIM48368.2019.8973365","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Self-Physical Rehabilitation System based on Hand Motion Sensor
Stroke is one of the diseases that cause high number of disability and mortality in Indonesia. The number of deaths from stroke in Indonesia reached 15.4% in almost all hospitals in Indonesia. One of the steps to help stroke patients to improve their motor function, speech, cognition, and other impaired functions is to conduct a series of post-stroke rehabilitation. Post-stroke rehabilitation can be done with direct supervision of the physiotherapist or performed alone at home or commonly called self-rehabilitation. Post-stroke rehabilitation has a variety of movements training, one of which is the movement of a finger. MedCap emerged as one of the tools that can help physiotherapists and patients to rehabilitate post-stroke fingers movement. MedCap which uses Leap Motion hand motion sensor can record a predetermined reference movement with a success rate of 62.35%. MedCap can also calculate the conformity of the reference movement and the real time movement of the patient using the Euclidean Distance method as a form of feedback to the physiotherapist and patient. The method used by MedCap can calculate the conformity of movement with real time movement with an average value of 43.2495%.