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ICS3211 lecture 11
1. ICS3211 - Intelligent
Interfaces II
Combining design with technology for effective human-
computer interaction
Week 11
Department of Intelligent Computer Systems,
University of Malta,
20161
2. Case Studies I: UIs &
Healthcare
Week 11 overview:
• Computer Prototyping: Task 4 / peer feedback
• A case study illustration: Mobile Interfaces & Healthcare
• A case study illustration: Medical Robotics
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3. Learning Outcomes
At the end of this session you should be able to:
• identify strengths and weaknesses in your peer’s IUI projects;
• compare and contrast the different projects and evaluate ways in
which they can improve;
• describe a number of case studies in which UIs are adapted to
improve healthcare systems;
• provide a critique of a healthcare system and propose
improvements to the UI to make it more intelligent.
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4. Introduction
• Class Activity: Prototyping exercise;
• go through your peers’ computer prototypes and
identify ways in which the UI can improve and
which can reinforce its intelligence.
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5. Peer Review
• Class Activity: Use the link:
https://goo.gl/forms/Rzg76Y6X9xZDTBsd2 to
provide feedback to your peers about their
prototype.
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6. Case Study I: An adaptive UI
in healthcare
• As people age, they require more frequent
demands on the healthcare system;
• Multiagent systems plus advances in computer
engineering can provide new technologies for more
services in the healthcare;
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7. Case Study I: An adaptive UI
in healthcare
• Increasing trend in healthcare monitoring with personal
technology, a movement sometimes referred to as
eHealth, and mHealth;
• A problem exists when a user is unable to operate the
interface to his or her technological device;
• What if an interface could adapt over time, to meet the
needs of a user? The theory behind such an interface
requires a multi-agent system to use a machine learning
technique that helps to build, test, and evaluate a policy
for each user;
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8. Case Study I: An adaptive UI
in healthcare
• What is an adaptive user interface?
• Two types of models: error & user interface;
• models describe the behaviour of users, which can
be later extended into the adaptive interfaces;
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9. Case Study I: An adaptive UI
in healthcare
• What model to adopt when the user is in poor
environmental conditions and has a disability which
prevents him from accessing a mobile interface?
• Theory of reinforcement learning;
• Models describe the behaviour of users, which can
be later extended into the adaptive interfaces;
• Body area sensor network
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14. Scenario 1
• A patient, Bob, spent the past 40 minutes walking around
the building. He would like to view his pulse data for the
past hour;
• Bob must interface with the smart device;
• Interaction requires launching an application interface to
the system. Once open, Bob taps the view health data
button to view health information. A dialog box will pop
up and requiring Bob to select the type of health
information he would like to view, in this case heart rate.
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16. Scenario 2
• A doctor, Alice, cares for several patients suffering
from heart complications. She would like to view the
pulse rates of four of her patients, for the past 24
hours;
• Alice interfaces with the system via the smart device;
• This interaction requires launching an application on
the device. Next, before performing any operations
with other users, Alice must prove her identity by
authenticating with credentials known only to her.
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17. • Once successfully authenticated, Alice requests to
view health data by tapping on the view health data
button.
• A dialog box appears asking Alice the type of health
information to view, she will specify heart rate.
• Another dialog box appears prompting Alice to input
the names of patients for whom to the data should be
gathered.
• Finally, the last dialog box appears asking for a time
range for the requested data.
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19. Medical Robots
• Medical nanotechnology is expected to employ
nanorobots that will be injected into the patient to
perform work at a cellular level;
• Dermables, digital stickers for the skin open a vast
range of possibilities. Netatmo’s JUNE bracelet
adds some class to UV monitoring and
UVSunSense make monitoring sun exposure fun.
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20. • Direct patient care robots: surgical robots (used for
performing clinical procedures), exoskeletons (for bionic
extensions of self like the Ekso suit), and prosthetics
(replacing lost limbs).
• Indirect patient care robots: pharmacy robots (streamlining
automation, autonomous robots for inventory control
reducing labor costs), delivery robots (providing medical
goods throughout a hospital autonomously), and disinfection
robots (interacting with people with known infectious
diseases such as healthcare-associated infections or HAIs).
• Home healthcare robots: robotic telepresence solutions
(addressing the ageing population with robotic assistance).
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21. Surgical Robots
• Soft robotic arms;
• Next generation 3DHD
visualization and
surface reconstruction;
• Micro-bots;
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