ABSTRACT
Medical advances have allowed people to live longer and this has presented different challenges to support them to keep living independently at home. The COVID-19 pandemic has affected the situation with less care staff and resources available for the elderly, which have also been spending more time at their homes without external contact. Elderly people are typically looked after by formal (professional carers) and informal (relatives and friends) carers. The work of carers has been increasingly supported using technologies for communication and wellbeing monitoring of Activities of Daily Living (ADLs) that elderly people perform in order to detect abnormal events that could negatively affect their wellbeing. This paper presents the design of a rule-based and ADL analysis system that takes data from different sensors as input and presents a number of visualisations in a dashboard as output. The dashboard is as user friendly as possible for both formal and informal carers of elderly people. It is intended that the proposed system can identify both immediate problems, but also trends and deviations from the individual’s norm, or that of a comparable cohort, which indicate the opportunity for pro-active care. This research has been done in collaboration with the Kraydel company, whose staff supported with ideas and with the commercial needs to be considered in the solution design presented in this paper. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
ABSTRACT
The COVID-19 pandemic crisis raised public health attention closer to our global society's demands. The disease proliferation occurs typically by droplet transmission, by being close to an infected person. Social distancing, a natural solution, is not always applicable to everyday needs, such as in the public transportation system, which is a space highly susceptible to viral proliferation. A set of ways to reduce proliferation in these infrastructures is by reinforcing facial masks usage, restraining symptomatic users, and reducing physical contact with public devices. Safe Gate, an Internet of Things (IoT) solution to enforce containment measures for disease proliferation, is proposed in this paper. This IoT solution is based on a network of edge computing devices used to control access to the entrance gate of the stations. The edge devices service samples an user's temperature and facial image to verify that body temperature is within normal bounds and the user is correctly wearing a face mask. The system is contact-free and does not require an active operator, with no personal data stored, preserving privacy. Additionally, it minimizes personnel involvement with passengers, ensuring staff protection. The research question is whether the solution with two levels of facial recognition using cognitive edge computing will meet the requirements of a real system. In addition to this question, a queuing model to verify the feasibility of the solution is presented and evaluates the operational impact on a real transportation system. © 2021 ACM.
ABSTRACT
Knowing what's on someone's mind might be challenging because only that person knows what's on their mind. COVID-19 advertisements are public service announcements, which raise public awareness of the issues occurred. A solution is needed to be able to find out what kind of advertisements attract someone to be memorized and to make COVID-19 advertisements even better. It is difficult to get the information in people's mind when they see the COVID-19 advertisement, a method and tools are needed to be able to mine the information which represent the human mind. We proposed the solution design based on Internet of Things (IoT) by using glasses to detect and record eye movements by using heat map. Accelerometer and gyroscope embedded in glasses are also needed to capture the head movement-orientation to perceive the gaze information to find out the pattern which COVID-19 advertisements can attract their attention to be memorized. © 2021 IEEE.