This document discusses two case studies about using adaptive user interfaces (UIs) to improve healthcare systems. The first case study describes an adaptive mobile interface for healthcare monitoring that can adjust over time based on a user's needs and abilities using machine learning. The second case study presents two scenarios where a patient and doctor interact with a smart device to view health data, including scenarios where authentication is required. The document also briefly outlines several applications of medical robots, including surgical robots, rehabilitation robots, and other uses of robotics in healthcare.

1. ICS3211 - Intelligent
Interfaces II
Combining design with technology for effective human-
computer interaction
Week 11 Department of AI, 2023
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2. Case Studies I: UIs &
Healthcare
Week 11 overview:
• 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:
• 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. 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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5. 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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6. 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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7. 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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12. 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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14. 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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15. • 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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17. 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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18. • 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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19. Surgical Robots
• Soft robotic arms;
• Next generation 3DHD
visualization and
surface reconstruction;
• Micro-bots;
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20. Rehabilitation Robots
• Neuro-rehabilitation
technology / neuro-
robotics;
• Virtual reality integrated
with rehabilitation
robots.
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21. Discussion Exercise
• Choose a reading from the list below and
summarise the role of AI in the case scenario
presented.
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