Intelligent User Interfaces and Their Evaluation: A Systematic Mapping Study.

Intelligent user interfaces (IUI) are driven by the goal of improvement in human-computer interaction (HCI), mainly improving user interfaces' user experience (UX) or usability with the help of artificial intelligence. The main goal of this study is to find, assess, and synthesize existing state-of-the-art work in the field of IUI with an additional focus on the evaluation of IUI. This study analyzed 211 studies published in the field between 2012 and 2022. Studies are most frequently tied to HCI and SE domains. Definitions of IUI were observed, showing that adaptation, representation, and intelligence are key characteristics associated with IUIs, whereas adaptation, reasoning, and representation are the most commonly used verbs in their description. Evaluation of IUI is mainly conducted with experiments and questionnaires, though usability and UX are not considered together in evaluations. Most evaluations (81% of studies) reported partial or complete improvement in usability or UX. A shortage of evaluation tools, methods, and metrics, tailored for IUI, is noticed. Most often, empirical data collection methods and data sources in IUI evaluation studies are experiment, prototype development, and questionnaire.

Sensors and Artificial Intelligence Methods and Algorithms for Human-Computer Intelligent Interaction: A Systematic Mapping Study.

To equip computers with human communication skills and to enable natural interaction between the computer and a human, intelligent solutions are required based on artificial intelligence (AI) methods, algorithms, and sensor technology. This study aimed at identifying and analyzing the state-of-the-art AI methods and algorithms and sensors technology in existing human-computer intelligent interaction (HCII) research to explore trends in HCII research, categorize existing evidence, and identify potential directions for future research. We conduct a systematic mapping study of the HCII body of research. Four hundred fifty-four studies published in various journals and conferences between 2010 and 2021 were identified and analyzed. Studies in the HCII and IUI fields have primarily been focused on intelligent recognition of emotion, gestures, and facial expressions using sensors technology, such as the camera, EEG, Kinect, wearable sensors, eye tracker, gyroscope, and others. Researchers most often apply deep-learning and instance-based AI methods and algorithms. The support sector machine (SVM) is the most widely used algorithm for various kinds of recognition, primarily an emotion, facial expression, and gesture. The convolutional neural network (CNN) is the often-used deep-learning algorithm for emotion recognition, facial recognition, and gesture recognition solutions.

Improved Bluetooth Low Energy Sensor Detection for Indoor Localization Services.

Advancements in protocols, computing paradigms, and electronics have enabled the development of wireless sensor networks (WSNs) with high potential for various location-based applications in different fields. One of the most important topics in WSNs is the localization in environments with sensor nodes being scattered randomly over a region. Localization techniques are often challenged by localization latency, efficient energy consumption, accuracy, environmental factors, and others. The objective of this study was to improve the technique for detecting the nearest Bluetooth Low Energy sensor, which would enable the development of more efficient mobile applications for location advertising at fairs, exhibitions, and museums. The technique proposed in this study was based on the iBeacon protocol, and it was tested in a controlled room with three environmental settings regarding the density of obstacles, as well as in a real-world setting at the Expo Museum at Postojna in Slovenia. The results of several independent measures, conducted in the controlled room and in the real-world environment, showed that the proposed algorithm outperformed the standard algorithm, especially in the environments with a medium or high densities of obstacles. The results of this study can be used for the more effective planning of placing beacons in space and for optimizing the algorithms for detecting transmitters in mobile location-based applications that provide users with contextual information based on their current location.

An empirical evaluation of a hands-free computer interaction for users with motor disabilities.

Standard computer input devices such as a mouse or a keyboard are not well suited to the needs of users with severe motor disabilities in their interaction with standard computer interfaces. The emergence of contemporary human computer interfaces has allowed for the development of innovative solutions for hands-free Human-Computer Interaction (HCI), which can improve the quality and accessibility of Information and Communication Technology (ICT) for motor-impaired users. The objectives of this study were to design, develop and evaluate a solution for a hands-free HCI, based on the Emotiv EPOC+ device, which, among other capabilities, also enables controlling the computer with facial expressions and motion sensors. Ten non-disabled adults and eight adults with a severe motor disability participated in an experiment to evaluate the proposed HCI solution. Eighteen users completed six experimental tasks successfully using both their existing computer use approach as well as the proposed hands-free computer use approach. The times necessary to complete the tasks were measured and analyzed, along with users' subjective observations about the difficulty level of both computer use approaches. Users' perceptions about the new hands-free computer use approach were assessed as well. Although there were no significant differences in both user types regarding the difficulty level in completing the tasks, disabled users solved the tasks with less effort. Positive perceptions about perceived usefulness, ease of use, appropriateness of the technology, and satisfaction with the proposed solution for touchless interaction were assessed for both user types. Scores were significantly higher for disabled users in the case of measuring the perceived usefulness, perceived ease of use and satisfaction with the solution. This study showed that users with severe motor difficulties find new HCI less challenging compared to their existing computer use approach than the non-disabled who use standard HCI. When compared with non-disabled users, the disabled ones can be equally effective when confronted with a new HCI technology. Future work is needed to improve the proposed solution and to analyze the impact of different factors on users with motor disabilities and their adoption of an innovative technology for touchless interaction with a computer.