Adaptive User Research Articles

Semantic speech analysis using machine learning and deep learning techniques: a comprehensive review

Human cognitive functions such as perception, attention, learning, memory, reasoning, and problem-solving are all significantly influenced by emotion. Emotion has a particularly potent impact on attention, modifying its selectivity in particular and influencing behavior and action motivation. Artificial Emotional Intelligence (AEI) technologies enable computers to understand a user's emotional state and respond appropriately. These systems enable a realistic dialogue between people and machines. The current generation of adaptive user interference technologies is built on techniques from data analytics and machine learning (ML), namely deep learning (DL) artificial neural networks (ANN) from multimodal data, such as videos of facial expressions, stance, and gesture, voice, and bio-physiological data (such as eye movement, ECG, respiration, EEG, FMRT, EMG, eye tracking). In this study, we reviewed existing literature based on ML and data analytics techniques being used to detect emotions in speech. The efficacy of data analytics and ML techniques in this unique area of multimodal data processing and extracting emotions from speech. This study analyzes how emotional chatbots, facial expressions, images, and social media texts can be effective in detecting emotions. PRISMA methodology is used to review the existing survey. Support Vector Machines (SVM), Naïve Bayes (NB), Random Forests (RF), Recurrent Neural Networks (RNN), Logistic Regression (LR), etc., are commonly used ML techniques for emotion extraction purposes. This study provides a new taxonomy about the application of ML in SER. The result shows that Long-Short Term Memory (LSTM) and Convolutional Neural Networks (CNN) are found to be the most useful methodology for this purpose.

Adaptive user interfaces in systems targeting chronic disease: a systematic literature review

eHealth technologies have been increasingly used to foster proactive self-management skills for patients with chronic diseases. However, it is challenging to provide each user with their desired support due to the dynamic and diverse nature of the chronic disease and its impact on users. Many such eHealth applications support aspects of “adaptive user interfaces”—interfaces that change or can be changed to accommodate the user and usage context differences. To identify the state of the art in adaptive user interfaces in the field of chronic diseases, we systematically located and analysed 48 key studies in the literature with the aim of categorising the key approaches used to date and identifying limitations, gaps, and trends in research. Our data synthesis is based on the data sources used for interface adaptation, the data collection techniques used to extract the data, the adaptive mechanisms used to process the data, and the adaptive elements generated at the interface. The findings of this review will aid researchers and developers in understanding where adaptive user interface approaches can be applied and necessary considerations for employing adaptive user interfaces to different chronic disease-related eHealth applications.

Character expression of a conversational robot for adapting to user personality

User adaptation is important in conversational robots to enhance the user experience and engagement. One way of user adaptation is to change the dialogue behavior. A character (e.g. extrovert or agreeable) can be defined to achieve human-like behaviors and user adaptation, and the appropriate character differs depending on each user. In this study, we investigate user adaptation by character expression of conversational robots. Our character expression model adopts the Big Five traits for controlling four dialogue behaviors: amount of utterance, backchannel frequency, filler frequency, and switching pause length. We cluster the system character and user personality into four classes based on an analysis of a speed dating dialogue corpus, and we found the best combinations between the system character and user personality. We implemented the character expression model into a laboratory guide and a chit-chat robot and conducted subjective experiments, where the subjects talked with robots with four different characters and evaluated their impressions of each character. The results shows that the Role model character was preferred for the task-oriented dialogue of the laboratory guide, but a robot character complementary to the subject personality was preferred for the non-task-oriented dialogue of the speed dating and chit-chat.

Structured dataset of human-machine interactions enabling adaptive user interfaces

This article introduces a dataset of human-machine interactions collected in a controlled and structured manner. The aim of this dataset is to provide insights into user behavior and support the development of adaptive Human-Machine Interfaces (HMIs). The dataset was generated using a custom-built application that leverages formally defined User Interfaces (UIs). The resulting interactions underwent processing and analysis to create a suitable dataset for professionals and data analysts interested in user interface adaptations. The data processing stage involved cleaning the data, ensuring its consistency and completeness. A data profiling analysis was conducted for checking the consistency of elements in the interaction sequences. Furthermore, for the benefit of researchers, the code used for data collection, data profiling, and usage notes on creating adaptive user interfaces are made available. These resources offer valuable support to those interested in exploring and utilizing the dataset for their research and development efforts in the field of human-machine interfaces.

A method of user recruitment and adaptation degree improvement via community collaboration in sparse mobile crowdsensing systems

The task allocation problem in sparse mobile crowdsensing is simplified as a subarea selection problem. However, the lack of participants in some high-value subareas leads to the low quality of the final inferred sensing map. To solve this problem, a method of user recruitment and adaptation degree improvement via community collaboration is proposed. Firstly, the adjacency matrix is constructed based on the social relationship of the participants, and then all the participants are classified into communities by the non-negative matrix factorization method of deep autoencoder-like; secondly, the perception platform matches the perception tasks with the centroids of the perception communities based on the different eigenvalues of the classified perception communities and the location characteristics of the perception tasks. After the matching is completed, some participants in the matched communities will be selected to complete the perceptual tasks under the constraint of perceptual cost; finally, the perceptual data provided by the participants is used to obtain the complete perceptual map using the compressed perceptual algorithm. We designed this user recruitment method to obtain high-quality sensing data by recruiting a small number of users after community classification based on their social relationships and then accurately inferring the entire perceptual map. The experimental results based on the Gowalla and U-Air datasets show that the user recruitment method proposed in this paper can infer accurate data with fewer sensing areas, which is significantly better than other comparison methods.

Design and implementation of an edge computing-based GPS tracking system

This paper presents a comprehensive study on the technical aspects of edge-enabled GPS tracking systems, their evolution, and an analysis of existing problem-solving approaches. It introduces mathematical models that simulate the operation of the hardware and software components of these systems. An adaptive user interface has been developed, enabling usage across various platforms such as smartphones and personal computers. The paper also explores innovative methods for visualizing the trajectory of a moving object on an electronic map. Specialized software for the Atmega162-16PU microcontroller has been developed to control the GSM module and GPS receiver. A novel method for data transmission from a GPS tracker to a web server is proposed. The paper concludes with the successful testing of two experimental GPS-tracker prototypes under uncertain conditions. The developed edge-enabled GPS tracking software and hardware show potential for monitoring moving objects within the coverage of GSM cellular networks.

A Perspective on Prosthetic Hands Control: From the Brain to the Hand

The human hand is a complex and versatile organ that enables humans to interact with the environment, communicate, create, and use tools. The control of the hand by the brain is a crucial aspect of human cognition and behaviour, but also a challenging problem for both neuroscience and engineering. The aim of this study is to review the current state of the art in hand and grasp control from a neuroscientific perspective, focusing on the brain mechanisms that underlie sensory integration for hand control and the engineering implications for developing artificial hands that can mimic and interface with the human brain. The brain controls the hand by processing and integrating sensory information from vision, proprioception, and touch, using different neural pathways. The user’s intention can be obtained to control the artificial hand by using different interfaces, such as electromyography, electroneurography, and electroencephalography. This and other sensory information can be exploited by different learning mechanisms that can help the user adapt to changes in sensory inputs or outputs, such as reinforcement learning, motor adaptation, and internal models. This work summarizes the main findings and challenges of each aspect of hand and grasp control research and highlights the gaps and limitations of the current approaches. In the last part, some open questions and future directions for hand and grasp control research are suggested by emphasizing the need for a neuroscientific approach that can bridge the gap between the brain and the hand.

Using emotions to cast light into the “black-box” of user adaptation and continued use of health information system

Although emotions are often evoked when medical professionals are required to employ new systems at work, their roles in information system post-adoption literature are relatively less examined. We draw on appraisal theories of emotions to unveil the emotional mechanisms underlying medical professionals' behaviors at different post-adoption stages. Results from data collected from a two-round survey demonstrate the roles of emotions such as irritation, interest, and satisfaction in explaining user adaptation at the initial post-adoption stage and the role of emotions such as pride and satisfaction in determining the continued use of technology following user adaptation at the later post-adoption stage.

User-Informed Adaptation in IoT Home Healthcare: Grounding Development in Empirical Evidence

IoT (Internet of Things)-enabled products are increasingly used by consumers and continuously propagate in daily life. Billions of networked objects not only increase the complexity of development but also raise user interaction and adaptation to individual needs. The more non-expert users are involved in decision making, interaction, and adaptation processes, the more user-centric IoT design is crucial, particularly when the number of elderly users is steadily increasing. In this paper, we study the capabilities of adopting IoT products through user-informed adaptation in a major IoT application domain, home healthcare. We review evidence from established practice in the field on how users can be supported when aiming to adapt medical IoT (M-IoT) home applications to their needs. We examine the empirically grounded use of IoT sensors and actuators, as well as the adaptation process users adopt when using an IoT application in a personalized environment. Our analysis (technological evidence) reveals various IoT devices that have already been applied in M-IoT adaptation settings to effectively support users. Our analysis reveals that only few empirically sound findings exist on how users actually perceive interactive adaptation features and redesign M-IoT applications. Based on the analysis of these empirically grounded findings, we suggest the development of a domain-specific user-centric adaptation feature. Specifically, we exemplify a tangible adaptation device for user-informed M-IoT application in home healthcare. It has been developed prototypically and tested in an environment for personalized home healthcare.

Semantic models and tools for designing adaptive user interfaces of intelligent systems

Objectives. Models and tools for designing adaptive user interfaces for intelligent systems are being developed. The relevance is determined by the need to reduce overhead costs and development time for user interfaces and to provide their adaptation to the specific characteristics of the user of the intelligent system.Methods. Existing approaches to designing user interfaces are being analyzed. A semantic model of an adaptive user interface for intelligent systems is proposed, implemented using a basic universal language for representing knowledge based on set theory and graph theory.Results. An adaptive user interface model for intelligent systems has been developed, which includes a knowledge base model of the user interface, an agent-oriented model of the user interface, and a library of reusable components that provide integration of the user interface into both individual intelligent systems and intelligent systems groups. A method of transferring user interface components within an intelligent systems group during the operation of an intelligent system has also been developed.Conclusion. Developed models and tools allow to simplify the reuse of user interface components together with knowledge base and problem-solving components in the design and development of individual intelligent systems, as well as in the design and development of a group of semantically compatible intelligent systems, ensuring automation of integration of user interfaces and their adaptation for each user. The developed set of user interface components has been included in a library of reusable user interface components. Further expansion of the set of components in the library and their integration into a group of semantically compatible intelligent systems is planned.

Towards the design of personalized adaptive user interfaces for smart TV viewers

Smart TV is a lean-back and shared device with diverse viewership, cultural acceptance, interaction modalities, input/output characteristics, and contextual use. Researchers, developers, and vendors constantly add new features and functionalities to its user interface (UI) to get higher market shares. However, the current smart TV UIs are static and follow a one-size-fits-all approach, where adding new features makes it cluttered and complex with limited learnability, usability, and greater cognitive overload. Another issue is the limited support for adaptive UIs to customize these features, functions, and services as per user needs. This article fills these gaps in the literature by designing a framework of personalized adaptive UIs for smart TV users capable of changing the UI layout and structure per the user needs and contextual details. The framework was tested on an Android-based smart TV and evaluated using a real-world dataset and an empirical study involving 75 household members in a mixed-mode questionnaire-based survey. The results were analyzed using Cronbach alpha, Kendall's tau-b, and principal component factor analysis. It was observed that personalized adaptive UIs are perceived positively in terms of attitude, usability, user experience, accessibility, learnability, user satisfaction, intension to use, and reduced cognitive overload.

A Multimodal User-Adaptive Recommender System

Traditional recommendation systems have predominantly relied on user-provided ratings as explicit input. Concurrently, visually aware recommender systems harness inherent visual cues within data to decode item characteristics and deduce user preferences. However, the untapped potential of incorporating item images into the recommendation process warrants investigation. This paper introduces an original convolutional neural network (CNN) architecture that leverages multimodal information, connecting user ratings with product images to enhance item recommendations. A central innovation of the proposed model is the User-Adaptive Filtering Module, a dynamic component that utilizes user profiles to generate personalized filters. Through meticulous visual influence analysis, the effectiveness of these filters is demonstrated. Furthermore, experimental results underscore the competitive performance of the approach compared to traditional collaborative filtering methods, thereby offering a promising avenue for personalized recommendations. This approach capitalizes on user adaptation patterns, enhancing the understanding of user preferences and visual attributes.

SmartLog: A Smart TV-Based Lifelogging System for Capturing, Storing, and Visualizing Watching Behavior

The modern smart TV can record viewers’ daily life-watching experiences called digital logs. These logs can be used for various purposes, including but not limited to recommendations, monitoring viewers, watching behavior, memory augmentation, e-learning, viewers’ privacy, and designing and developing adaptive User Interfaces (UI). Much work is available on lifelogging technologies related to smartphones, fitness gadgets, and wearable and non-wearable devices. However, little attention has been given to smart TV-based lifelogging systems. In this paper, we proposed a TV-based SmartLog framework for logging specific information about watching experiences. The proposed work is implemented and installed on Android-based Smart TV. We logged various activities of individual viewers and other household members and their environmental contexts. We empirically evaluated the proposed solution by analyzing data from 75 household viewers of different age groups. These groups include teenagers, homemakers, senior citizens, literate, and educated. We carried out various tests on the collected data, ie, descriptive tabulation, Kendall’s tau-b, Cronbach alpha, and Principal Component Factor Analysis (PCFA). The findings suggest that the SmartLog app received a positive perception regarding attitude, user satisfaction, ease of use, and intention to use. The proposed solution can be used to assist household viewers in terms of personalization, usability, accessibility, user experience, learnability, and reducing cognitive overload while interacting with smart TV.

ULOGA SOCIJALNOG PEDAGOGA U USTANOVAMA ZA SMJEŠTAJ STARIJIH OSOBA

Upon entering a home for the elderly, a person may feel rejected by their relatives and friends. That is why the support of the social pedagogue is very important for them when they enter the institution, the way of communication, familiarization with the rights and obligations in the institution, but their better functioning. The aim of the paper is to examine the role of the social pedagogue in relation to other employees in institutions for housing the elderly, and to examine the significance of the social pedagogue for the functioning and assistance in the adaptation of users when entering an institution for the housing of the elderly. 100 respondents participated in the research. For the purposes of the research, two measuring instruments were used: the Multidimensional Scale of Perceived Social Support - MSPSS and the HCAHPS Survey. The results of the research show that the social pedagogue is an important support link for the elderly, and that he has the competencies and knowledge to work with the elderly, considering their personality structure. The results of the t-test (t=4.778; df=87; p=0.000) show that there is a statistically significant difference in relation to the adaptation of users and the professional competence of social pedagogues who play a crucial role in facilitating the adaptation of users of the home.

Using Networked Ontologies to Support the Development of Software Systems with Adaptive User Interface

The new ways of interacting with computers, smartphones, and other devices have brought new challenges, such as the need to ensure that different types of users can easily use the same system. Adaptive User Interface (AUI) systems have been recognized as a solution to this matter. They change the user interface to better meet the needs of different users. However, developing such systems is not trivial. It is necessary to capture the users’ characteristics and preferences and constantly adapt the system accordingly. In this paper, we discuss the use of ontologies to support the development of AUI systems. We argue that by providing structured knowledge about such systems, ontologies help understand how they work and offer a basis to structure them, identify the necessary adaptations and implement mechanisms to make them happen in run-time. We have explored the use of ontologies from an ontology network (i.e., networked ontologies) to develop a social network about academic subjects that automatically adapts its interface according to the low vision and colorblind user’s needs and usage characteristics. This exploratory study showed that using networked ontologies to develop an AUI system is useful and feasible. The ontology was useful at the conceptual level by serving as a basis to define the system’s structural model and at the operational level by providing the semantics used in a reasoning engine to adapt the UI at run-time. The first version of an ontology-based process to guide the development of AUI systems emerged from this experience and it is also presented in this paper.

Adaptive User Pairing in Multi-IRS-Aided Massive MIMO-NOMA Networks: Spectral Efficiency Maximization and Deep Learning Design

In this paper, we propose an adaptive user pairing (AUP) scheme in multi-intelligent reflecting surface (IRS)-aided massive multiple-input multiple-output (MIMO)-non-orthogonal multiple access (NOMA) networks. In the AUP scheme, two users with different channel conditions are selected for user pairing while multiple IRSs assist to improve received signal quality at users. We consider the problem of jointly optimizing the precoding matrix, the phase shift of IRSs, and the user pairing element to maximize the overall spectral efficiency (SE) subject to the maximum power budget at the base station (BS) and user-specific quality-of-service (QoS). The SE problem formulated as the maximization of non-concave functions involves a mixed-integer program, which is very challenging to solve optimally. To tackle this problem, we first relax the user pairing elements to be continuous and then transform the formulated problem into an equivalent non-convex problem with a more tractable form. We then apply the iterative algorithm (IA) with low complexity to guarantee convergence at a relative optimum. Towards real-time optimization, we propose a deep learning (DL) framework to predict the optimal solution of the precoding matrix, the phase shift of IRSs, and user pairing elements according to the user’s locations and channel gains. Compared to the conventional optimization method, the DL-based optimization framework can achieve the optimal solution within a very short time via an efficient inference process. Numerical results verify that the proposed algorithm improves the SE over state-of-the-art approaches. Moreover, the effects of essential parameters such as the total BS transmit power, the number of UEs, IRSs, and BS’s antennas on the system are discussed and evaluated to show the effectiveness of the proposed scheme in balancing resource utilization.

Adaptive User Interfaces: Personalizing User Experience User Profiling and Modeling

Adaptive User Interfaces: Personalizing User Experience User Profiling and Modeling

Adaptive User Interfaces: Personalizing User Experience User Profiling and Modeling

Adaptive User Interfaces: Personalizing User Experience User Profiling and Modeling

Real-World Implementation of Best-Evidence Cancer Distress Management: Truly Comprehensive Cancer Care.

Cancer distress management is an evidence-based component of comprehensive cancer care. Group-delivered cognitive behavioral therapy for cancer distress (CBT-C) is the first distress treatment associated with replicated survival advantages in randomized clinical trials. Despite research supporting patient satisfaction, improved outcomes, and reduced costs, CBT-C has not been tested sufficiently within billable clinical settings, profoundly reducing patient access to best-evidence care. This study aimed to adapt and implement manualized CBT-C as a billable clinical service. A stakeholder-engaged, mixed-methods, hybrid implementation study design was used, and the study was conducted in 3 phases: (1) stakeholder engagement and adaptation of CBT-C delivery, (2) patient and therapist user testing and adaptation of CBT-C content, and (3) implementation of practice-adapted CBT-C as a billable clinical service focused on evaluation of reach, acceptability, and feasibility across stakeholder perspectives. A total of 40 individuals and 7 interdisciplinary group stakeholders collectively identified 7 primary barriers (eg, number of sessions, workflow concerns, patient geographic distance from center) and 9 facilitators (eg, favorable financial model, emergence of oncology champions). CBT-C adaptations made before implementation included expanding eligibility criteria beyond breast cancer, reducing number of sessions to 5 (10 total hours), eliminating and adding content, and revising language and images. During implementation, 252 patients were eligible; 100 (40%) enrolled in CBT-C (99% covered by insurance). The primary reason for declining enrollment was geographic distance. Of enrollees, 60 (60%) consented to research participation (75% women; 92% white). All research participants completed at least 60% of content (6 of 10 hours), with 98% reporting they would recommend CBT-C to family and friends. CBT-C implementation as a billable clinical service was acceptable and feasible across cancer care stakeholder measures. Future research is needed to replicate acceptability and feasibility results in more diverse patient groups, test effectiveness in clinical settings, and reduce barriers to access via remote delivery platforms.

Proactive Control for Online Individual User Adaptation in a Welfare Robot Guidance Scenario: Toward Supporting Elderly People

Due to demographic change, health and elderly care systems are facing a shortage of qualified caregivers. This issue can be addressed by introducing welfare robots into people’s homes, hospitals, and care institutions. To provide useful support, such robots must adapt to individual users and smoothly interact with them. From this perspective, we present advances on the development of proactive control for online individual user adaptation in a welfare robot guidance scenario, with the integration of three main modules: 1) navigation control; 2) visual human detection; and 3) temporal error correlation-based neural learning. The proposed control approach can drive a mobile robot to autonomously navigate in relevant indoor environments. At the same time, it can predict human walking speed based on visual information without prior knowledge of personality and preferences (i.e., walking speed). The robot then uses this prediction to continuously adapt its speed to individual users in a proactive online manner. We validate the performance of the proposed proactive robot control in different real-world environments with various users, including an elderly resident of a Danish elderly care center. The results show that the robot successfully and smoothly guided various users of different ages and average walking speeds (e.g., 0.2 m/s, 0.7 m/s, and 1.1 m/s) to target locations over distances of 25–60 m. All in all, this study captures a wide range of research from robot control technology development to technological validity in a relevant environment and system prototype demonstration in an operational environment (i.e., an elderly care center).