Scalable Human-Machine Interaction System for Real-Time Care in the Internet of Health Things
| dc.contributor.author | Nasr Mohamed, Mahmoud | |
| dc.date.accessioned | 2020-08-28T17:13:35Z | |
| dc.date.available | 2020-08-28T17:13:35Z | |
| dc.date.issued | 2020-08-28 | |
| dc.date.submitted | 2020-08-21 | |
| dc.description.abstract | The rise in numbers of individuals with weak immunity around the world and the aging of populations put an ever-growing pressure on healthcare and inevitably increases its cost. This phenomenon leads to larger portions of the population to which quality healthcare is not provided. To fight this trend, technological advancements in the Internet of Health Things aim to integrate smart sensors and devices to continuously monitor and assess the status of patients and older adults from the comfort of their own home at a fraction of the cost. Although solving specific problems each at a time advances the field and takes us a step closer to autonomous home care systems, the solution to these issues needs to consider the much larger picture to unify the approaches and cultivate benefits of many intelligent, but stand-alone, systems. The current work aims to explore the field of Internet of Health Things and its application to remote health monitoring and ambient assisted living for older adults. Picking up from where previous literature left off, this thesis proposes a multi-layered framework that provides a comprehensive solution to continuous healthcare. In particular, the framework was created with modularity, scalability, and expandability as the main priorities; to offer an all-purpose remedy to the problems in hand. To this end, the internal mechanisms of the framework are described in detail and the system is applied to remote health monitoring and ambient assisted living environments by interchanging its components. The implementations presented in this thesis expose the capability of the framework to harvest power of existing intelligent devices. Moreover, the two systems implemented consider multi-modal and natural human-machine interaction techniques that provide the user with the choice of their preferred interaction method. The main advantage of the proposed framework is that it offers an all-in-one solution to providing continuous healthcare without sacrificing the quality of care provided. On the contrary, the solution in this work allows deeper understanding of user's health, personalization, real-time analytics and recommendations, as well as aid for activities of daily living with state of the art technologies. | en |
| dc.identifier.uri | http://hdl.handle.net/10012/16185 | |
| dc.language.iso | en | en |
| dc.pending | false | |
| dc.publisher | University of Waterloo | en |
| dc.subject | Ambient Assisted Living | en |
| dc.subject | Remote Health Monitoring | en |
| dc.subject | Human-Machine Interaction | en |
| dc.subject | Internet of Things | en |
| dc.subject | Internet of Health Things | en |
| dc.subject | Social Robots | en |
| dc.subject | Robotics | en |
| dc.subject | Speech Interaction | en |
| dc.title | Scalable Human-Machine Interaction System for Real-Time Care in the Internet of Health Things | en |
| dc.type | Master Thesis | en |
| uws-etd.degree | Master of Applied Science | en |
| uws-etd.degree.department | Electrical and Computer Engineering | en |
| uws-etd.degree.discipline | Electrical and Computer Engineering | en |
| uws-etd.degree.grantor | University of Waterloo | en |
| uws.contributor.advisor | Fakhri, Karray | |
| uws.contributor.affiliation1 | Faculty of Engineering | en |
| uws.peerReviewStatus | Unreviewed | en |
| uws.published.city | Waterloo | en |
| uws.published.country | Canada | en |
| uws.published.province | Ontario | en |
| uws.scholarLevel | Graduate | en |
| uws.typeOfResource | Text | en |