Joana Marques, S. Ramos, Milton P. Macedo, H. P. da Silva
{"title":"Study of Mechanomyographic Alternatives to EMG Sensors for a Low-Cost Open Source Bionic Hand","authors":"Joana Marques, S. Ramos, Milton P. Macedo, H. P. da Silva","doi":"10.1007/978-3-030-30335-8_1","DOIUrl":"https://doi.org/10.1007/978-3-030-30335-8_1","url":null,"abstract":"","PeriodicalId":420268,"journal":{"name":"5th EAI International Conference on IoT Technologies for HealthCare","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131742860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mahmoud S. Assaf, Aïcha Rizzotti-Kaddouri, Magdalena Punceva
{"title":"Sleep Detection Using Physiological Signals from a Wearable Device","authors":"Mahmoud S. Assaf, Aïcha Rizzotti-Kaddouri, Magdalena Punceva","doi":"10.4108/EAI.21-11-2018.2281067","DOIUrl":"https://doi.org/10.4108/EAI.21-11-2018.2281067","url":null,"abstract":"Internet of Things for medical devices is revolutionizing healthcare industry by providing platforms for data collection via cloud gateways and analytic. In this paper, we propose a process for developing a proof of concept solution for sleep detection by observing a set of ambulatory physiological parameters in a completely non-invasive manner. Observing and detecting the state of sleep and also its quality, in an objective way, has been a challenging problem that impacts many medical fields. With the solution presented here, we propose to collect physiological signals from wearable devices, which in our case consist of a smart wristband equipped with sensors and a protocol for communication with a mobile device. With machine learning based algorithms, that we developed, we are able to detect sleep from wakefulness in up to 93% of cases. The results from our study are promising with a potential for novel insights and effective methods to manage sleep disturbances and improve sleep quality.","PeriodicalId":420268,"journal":{"name":"5th EAI International Conference on IoT Technologies for HealthCare","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127116690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}