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.