Applying the User-Centered Design approach for Prototyping the Interfaces of an Intelligent Emergency Management System

In today’s disaster management context, decision-making and information management is a crucial aspect, given the complexity of the tasks the decision-maker has, as well as the sheer influx of information coming in at any given time. As such there is a need to develop a system that can aid both the decision makers in the command post but can also collect the information gathered by the responders on the field. This system should also aid the decision maker by providing counselling according a set of rules, giving the system an intelligent aspect. Thusly THEMIS is born, an intelligent system to support decision making in crisis scenarios. As any given system must have an interface, the usability and user experience are a concern, but given the nature of crisis scenarios, this aspect of user interfaces becomes much more critical. It is in this context that this dissertation’s goal becomes clear: design and test the interface prototype of an emergency management intelligent system, following the User-Centered Design framework. With this goal in mind, the steps of the framework were followed, by beginning to understand the user, the context of use, resulting in understanding the user’s needs. From here, the system requirements emerged, and paper prototyping began. After validation with experts and possible users, the interfaces were prototyped digitally for both the desktop and mobile system applications. This was followed by usability tests, using the Cognitive Walkthrough method, the System Usability Score and the User Experience Questionnaire. In order to complement the testing phase, eye tracking data was gathered during the desktop version’s usability tests, which gave further insight about user behaviour. As such, it was concluded that prototypes scored highly both for usability and user experience, and there was an overall improvement on the various versions of both the desktop and mobile apps. The tests with eye tracking also allowed to identify a few issues that otherwise couldn’t be detected, namely key items the users were missing on the interfaces

User Feedback in Controllable and Explainable Social Recommender Systems: a Linguistic Analysis

Controllable and explainable intelligent user interfaces have been used to provide transparent recommendations. Many researchers have explored interfaces that support user control and provide explanations of the recommendation process and models. To extend the works to real-world decision-making scenarios, we need to understand further the users’ mental models of the enhanced system components. In this paper, we make a step in this direction by investigating a free form feedback left by users of social recommender systems to specify the reasons of selecting prompted social recommendations. With a user study involving 50 subjects (N=50), we present the linguistic changes in using controllable and explainable interfaces for a social information-seeking task. Based on our findings, we discuss design implications for controllable and explainable recommender systems

Night optimised care technology for users needing assisted lifestyles

There is growing interest in the development of ambient assisted living services to increase the quality of life of the increasing proportion of the older population. We report on the Night Optimised Care Technology for UseRs Needing Assisted Lifestyles project, which provides specialised night time support to people at early stages of dementia. This article explains the technical infrastructure, the intelligent software behind the decision-making driving the system, the software development process followed, the interfaces used to interact with the user, and the findings and lessons of our user-centred approach

Night optimised care technology for users needing assisted lifestyles

There is growing interest in the development of ambient assisted living services to increase the quality of life of the increasing proportion of the older population. We report on the Night Optimised Care Technology for UseRs Needing Assisted Lifestyles project, which provides specialised night time support to people at early stages of dementia. This article explains the technical infrastructure, the intelligent software behind the decision-making driving the system, the software development process followed, the interfaces used to interact with the user, and the findings and lessons of our user-centred approach

Data Collection and Analysis Techniques for Solar Car Telemetry Data

Data collected from a solar car is monitored in real-time, which allows for intelligent decision making, efficient debugging, and high-quality testing for solar car teams. This paper compares three databases (MySQL, PostgreSQL, and MongoDB) to determine the optimal database system that should be used at solar car competitions. Each database system was tested using simulated solar car data to measure read and write speeds, and quality of performance on a low-power computer. Data were analyzed and displayed with custom interfaces to improve the user experience at solar car competitions

Enabling human-centered AI: A new junction and shared journey between AI and HCI communities

Artificial intelligence (AI) has brought benefits, but it may also cause harm if it is not appropriately developed. Current development is mainly driven by a "technology-centered" approach, causing many failures. For example, the AI Incident Database has documented over a thousand AI-related accidents. To address these challenges, a human-centered AI (HCAI) approach has been promoted and has received a growing level of acceptance over the last few years. HCAI calls for combining AI with user experience (UX) design will enable the development of AI systems (e.g., autonomous vehicles, intelligent user interfaces, or intelligent decision-making systems) to achieve its design goals such as usable/explainable AI, human-controlled AI, and ethical AI. WHile HCAI promotion continues, it has not specifically addressed the collaboration between AI and human-computer interaction (HCI) communities, resulting in uncertainty about what action should be taken by both sides to apply HCAI in developing AI systems. This Viewpoint focuses on the collaboration between the AI and HCI communities, which leads to eight recommendations for effective collaboration to enable HCAI in developing AI systems

User Feedback in Controllable and Explainable Social Recommender Systems: a Linguistic Analysis. In: Proceedings of Joint Workshop on Interfaces and Human Decision Making for Recommender Systems

Controllable and explainable intelligent user interfaces have been used to provide transparent recommendations. Many researchers have explored interfaces that support user control and provide explanations of the recommendation process and models. To extend the works to real-world decision-making scenarios, we need to understand further the users' mental models of the enhanced system components. In this paper, we make a step in this direction by investigating a free form feedback left by users of social recommender systems to specify the reasons of selecting prompted social recommendations. With a user study involving 50 subjects (N=50), we present the linguistic changes in using controllable and explainable interfaces for a social information-seeking task. Based on our findings, we discuss design implications for controllable and explainable recommender systems

IRS-HD: an intelligent personalized recommender system for heart disease patients in a tele-health environment

The use of intelligent technologies in clinical decision making support may play a promising role in improving the quality of heart disease patients’ life and helping to reduce cost and workload involved in their daily health care in a tele-health environment. The objective of this demo proposal is to demonstrate an intelligent prediction system we developed, called IRS-HD, that accurately advises patients with heart diseases concerning whether they need to take the body test today or not based on the analysis of their medical data during the past a few days. Easy-to-use user friendly interfaces are developed for users to supply necessary inputs to the system and receive recommendations from the system. IRS-HD yields satisfactory recommendation accuracy, offers a promising way for reducing the risk of incorrect recommendations, as well saves the workload for patients to conduct body tests every day

Microservices and Machine Learning Algorithms for Adaptive Green Buildings

In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings

User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care