{"title":"Context-Aware Multimodal Robotic Health Assistant","authors":"Vidyavisal Mangipudi, Raj Tumuluri","doi":"10.1145/2663204.2669627","DOIUrl":null,"url":null,"abstract":"Reduced adherence to medical regimen has led to poorer health, more frequent hospitalization and costs the American economy over $290 Billion annually. EasyHealth Assistant (EHA) is a context aware and interactive robot that helps patients receive their medication in the prescribed dosage at the right time. Additionally, EHA features multimodal elements such as Face Recognition, Speech Recognition + TTS, Motion Sensing and MindWave (EEG) interactions that were developed using W3C MMI Architecture and Markup Languages. EHA improves the Caregiver/ Doctor -- Patient collaboration with tools like Remote control and Video conference. It also provides the Caregivers with real-time statistics and allows easy monitoring of medical adherence and health vitals, which should result in improved outcome for the patient.","PeriodicalId":389037,"journal":{"name":"Proceedings of the 16th International Conference on Multimodal Interaction","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 16th International Conference on Multimodal Interaction","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2663204.2669627","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Reduced adherence to medical regimen has led to poorer health, more frequent hospitalization and costs the American economy over $290 Billion annually. EasyHealth Assistant (EHA) is a context aware and interactive robot that helps patients receive their medication in the prescribed dosage at the right time. Additionally, EHA features multimodal elements such as Face Recognition, Speech Recognition + TTS, Motion Sensing and MindWave (EEG) interactions that were developed using W3C MMI Architecture and Markup Languages. EHA improves the Caregiver/ Doctor -- Patient collaboration with tools like Remote control and Video conference. It also provides the Caregivers with real-time statistics and allows easy monitoring of medical adherence and health vitals, which should result in improved outcome for the patient.