{"title":"在没有特殊测试的情况下估计中风患者的身体能力","authors":"A. Derungs, J. Seiter, C. Schuster-Amft, O. Amft","doi":"10.1145/2802083.2808412","DOIUrl":null,"url":null,"abstract":"We estimate the Extended Barthel Index (EBI) in patients after stroke using inertial sensor measurements acquired during daily activity, rather than specific assessments. The EBI is a standard clinical assessment showing patient independence in handling everyday tasks. Our work aims at providing a continuous ability estimate for patients and therapists that could be used without expert supervision. We extract nine activity primitives (AP), including sitting, standing, transition, etc. from the continuous sensor data using basic rules that do not require data-based training. Using the relative duration of activity primitives, we evaluate the EBI score estimation using two regression methods: Generalised Linear Models (GLM) and Support-Vector Regression (SVR). We evaluated our approaches in full-day study recordings from 11 stroke patients with totally 102 days in ambulatory rehabilitation in a day-care centre. Our results show that EBI can be estimated from the activity primitives with approximately 12% relative error on average for all study participants using SVR. Our results indicate that EBI can be estimated in daily life activity, thus supporting patients and therapists in tracking rehab progress.","PeriodicalId":372395,"journal":{"name":"Proceedings of the 2015 ACM International Symposium on Wearable Computers","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Estimating physical ability of stroke patients without specific tests\",\"authors\":\"A. Derungs, J. Seiter, C. Schuster-Amft, O. Amft\",\"doi\":\"10.1145/2802083.2808412\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We estimate the Extended Barthel Index (EBI) in patients after stroke using inertial sensor measurements acquired during daily activity, rather than specific assessments. The EBI is a standard clinical assessment showing patient independence in handling everyday tasks. Our work aims at providing a continuous ability estimate for patients and therapists that could be used without expert supervision. We extract nine activity primitives (AP), including sitting, standing, transition, etc. from the continuous sensor data using basic rules that do not require data-based training. Using the relative duration of activity primitives, we evaluate the EBI score estimation using two regression methods: Generalised Linear Models (GLM) and Support-Vector Regression (SVR). We evaluated our approaches in full-day study recordings from 11 stroke patients with totally 102 days in ambulatory rehabilitation in a day-care centre. Our results show that EBI can be estimated from the activity primitives with approximately 12% relative error on average for all study participants using SVR. Our results indicate that EBI can be estimated in daily life activity, thus supporting patients and therapists in tracking rehab progress.\",\"PeriodicalId\":372395,\"journal\":{\"name\":\"Proceedings of the 2015 ACM International Symposium on Wearable Computers\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-09-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2015 ACM International Symposium on Wearable Computers\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2802083.2808412\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2015 ACM International Symposium on Wearable Computers","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2802083.2808412","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Estimating physical ability of stroke patients without specific tests
We estimate the Extended Barthel Index (EBI) in patients after stroke using inertial sensor measurements acquired during daily activity, rather than specific assessments. The EBI is a standard clinical assessment showing patient independence in handling everyday tasks. Our work aims at providing a continuous ability estimate for patients and therapists that could be used without expert supervision. We extract nine activity primitives (AP), including sitting, standing, transition, etc. from the continuous sensor data using basic rules that do not require data-based training. Using the relative duration of activity primitives, we evaluate the EBI score estimation using two regression methods: Generalised Linear Models (GLM) and Support-Vector Regression (SVR). We evaluated our approaches in full-day study recordings from 11 stroke patients with totally 102 days in ambulatory rehabilitation in a day-care centre. Our results show that EBI can be estimated from the activity primitives with approximately 12% relative error on average for all study participants using SVR. Our results indicate that EBI can be estimated in daily life activity, thus supporting patients and therapists in tracking rehab progress.