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ICS3211 - Intelligent
Interfaces IICombining design with technology for effective
human-computer interaction
Week 11Department of Intelligent Computer
Systems,University of Malta,
2016
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
Learning OutcomesAt 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.
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.
Peer Review
• Class Activity: Use the link: https://goo.gl/forms/Rzg76Y6X9xZDTBsd2 to provide feedback to your peers about their prototype.
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;
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;
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;
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
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.
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.
• 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.
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.
• 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).
Surgical Robots• Soft robotic arms;
• Next generation 3DHD visualization and surface reconstruction;
• Micro-bots;
Rehabilitation Robots
• Neuro-rehabilitation technology / neuro-robotics;
• Virtual reality integrated with rehabilitation robots.