Mais Al Atallah, Zainab Jamil, Hania Khafagy, Mehnaz Ummar, F. Aloul, A. Sagahyroon
{"title":"脑卒中康复智能监测系统","authors":"Mais Al Atallah, Zainab Jamil, Hania Khafagy, Mehnaz Ummar, F. Aloul, A. Sagahyroon","doi":"10.1109/IoTaIS53735.2021.9628644","DOIUrl":null,"url":null,"abstract":"Stroke survivors are vulnerable to post-stroke upper limb disabilities and physiotherapy is typically recommended to improve their movement. This paper introduces a patient-centered smart system where patients performing rehabilitation exercises can receive automated visuals of their improvements in the comfort of their homes. Moreover, the patient’s health is also monitored based on their heart rate, recommendations regarding their improvement based on the exercises performed are provided and the patient’s likelihood of stroke recurrence is predicted by the system. The system uses accelerometer and heart rate readings from a smartwatch along with readings from a stretch sensor attached to an exercise band. These readings are stored in the cloud and real-time databases, which are retrieved in the mobile application, where data is processed using algorithms to assess the improvement as well as generate recommendation and prediction models.","PeriodicalId":183547,"journal":{"name":"2021 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Smart Monitoring System for Stroke Rehabilitation\",\"authors\":\"Mais Al Atallah, Zainab Jamil, Hania Khafagy, Mehnaz Ummar, F. Aloul, A. Sagahyroon\",\"doi\":\"10.1109/IoTaIS53735.2021.9628644\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Stroke survivors are vulnerable to post-stroke upper limb disabilities and physiotherapy is typically recommended to improve their movement. This paper introduces a patient-centered smart system where patients performing rehabilitation exercises can receive automated visuals of their improvements in the comfort of their homes. Moreover, the patient’s health is also monitored based on their heart rate, recommendations regarding their improvement based on the exercises performed are provided and the patient’s likelihood of stroke recurrence is predicted by the system. The system uses accelerometer and heart rate readings from a smartwatch along with readings from a stretch sensor attached to an exercise band. These readings are stored in the cloud and real-time databases, which are retrieved in the mobile application, where data is processed using algorithms to assess the improvement as well as generate recommendation and prediction models.\",\"PeriodicalId\":183547,\"journal\":{\"name\":\"2021 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IoTaIS53735.2021.9628644\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IoTaIS53735.2021.9628644","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Stroke survivors are vulnerable to post-stroke upper limb disabilities and physiotherapy is typically recommended to improve their movement. This paper introduces a patient-centered smart system where patients performing rehabilitation exercises can receive automated visuals of their improvements in the comfort of their homes. Moreover, the patient’s health is also monitored based on their heart rate, recommendations regarding their improvement based on the exercises performed are provided and the patient’s likelihood of stroke recurrence is predicted by the system. The system uses accelerometer and heart rate readings from a smartwatch along with readings from a stretch sensor attached to an exercise band. These readings are stored in the cloud and real-time databases, which are retrieved in the mobile application, where data is processed using algorithms to assess the improvement as well as generate recommendation and prediction models.