536
B. Farahani et al.
11.6.3 Interfaces and Human Factor Engineering
It is important to consider additional factors such as human acceptability of a
particular system and the level to which that system supports human interactions.
The interface of sensors, front-end technology, tablets, smartphones, computers, and
other interactions is one of the main factors in IoT e-Health. In order for individuals
with little experience using technology to be able to use IoT e-Health devices, it
is important that end users be able to train themselves how to use high-tech tools
intuitively. Generally, end users have little knowledge about sensor syncing, wireless
networks, or other technical operations. Therefore, when devices are utilized in
remote environments, it is important that establishing the e-Health system be as
forthright and autonomous as possible. For example, geriatric populations are one
of the largest stakeholder groups in IoT e-Health, and device interfaces must be
user-friendly, requiring minimal assistance from experts. Successful human factor
engineering includes participatory design that encourages end users to participate
with the design team by providing continuous feedback regarding ease of use,
dislikes, likes, and comfort levels.
11.6.4 Scalability
IoT on a smaller scale requires that portable devices contain data collection
sensors and that centralized servers process user requests to ensures all users are
able to access medical services using compact devices like smartphones. This
functionality can be scaled up to serve an entire hospital so that patients can utilize
medical services, receive status updates, and benefit from continuous monitoring.
In addition, this model could be scaled up to serve a whole city, assuming antenna
and sensors in the city are able to gather data. Smart Big Data algorithms and APIs
could be used to process data and evaluate user requests while smart interfaces can
provide users with the status of requests in real time. A smart, e-Health-based city
could collect and process all data via smartphones and mobile applications, sending
feedback to patients so that each is aware of personal health status and has access to
results of medical testing or examinations seamlessly. Utilizing IoT medical services
saves patients’ time waiting for appointments or results and provides clear access to
basic medical services and resources. Scaling up from a small network to the level
of a smart city has the potential to improve efficiency, support relationship building,
and promote greater trust between medical professionals and patients [1, 9].
B. Farahani et al.
11.6.3 Interfaces and Human Factor Engineering
It is important to consider additional factors such as human acceptability of a
particular system and the level to which that system supports human interactions.
The interface of sensors, front-end technology, tablets, smartphones, computers, and
other interactions is one of the main factors in IoT e-Health. In order for individuals
with little experience using technology to be able to use IoT e-Health devices, it
is important that end users be able to train themselves how to use high-tech tools
intuitively. Generally, end users have little knowledge about sensor syncing, wireless
networks, or other technical operations. Therefore, when devices are utilized in
remote environments, it is important that establishing the e-Health system be as
forthright and autonomous as possible. For example, geriatric populations are one
of the largest stakeholder groups in IoT e-Health, and device interfaces must be
user-friendly, requiring minimal assistance from experts. Successful human factor
engineering includes participatory design that encourages end users to participate
with the design team by providing continuous feedback regarding ease of use,
dislikes, likes, and comfort levels.
11.6.4 Scalability
IoT on a smaller scale requires that portable devices contain data collection
sensors and that centralized servers process user requests to ensures all users are
able to access medical services using compact devices like smartphones. This
functionality can be scaled up to serve an entire hospital so that patients can utilize
medical services, receive status updates, and benefit from continuous monitoring.
In addition, this model could be scaled up to serve a whole city, assuming antenna
and sensors in the city are able to gather data. Smart Big Data algorithms and APIs
could be used to process data and evaluate user requests while smart interfaces can
provide users with the status of requests in real time. A smart, e-Health-based city
could collect and process all data via smartphones and mobile applications, sending
feedback to patients so that each is aware of personal health status and has access to
results of medical testing or examinations seamlessly. Utilizing IoT medical services
saves patients’ time waiting for appointments or results and provides clear access to
basic medical services and resources. Scaling up from a small network to the level
of a smart city has the potential to improve efficiency, support relationship building,
and promote greater trust between medical professionals and patients [1, 9].