Adaptive User Research Articles

AI-Based Culture Independent Pervasive M-Learning Prototype Using UI Plasticity Design

This paper explains the development of a culturally inclusive ubiquitous M-Learning platform (“Mobile Academy”) with an AI-based adaptive user interface. The rationale and need for this research and development are justif... | Find, read and cite all the research you need on Tech Science Press

The role of user adaptation and trust in understanding continuance intention towards mobile shopping: An extended expectation-confirmation model

With the rapid growth and intense competition of the mobile commerce market, mobile vendors consider retaining current users and facilitating them to continue shopping as vital to sustain profitabi...

User Adaptation to Closed-Loop Decoding of Motor Imagery Termination.

Here, we provide insights in the decoding of motor imagery termination and investigate the use of such decoder in closed-loop BMI. Participants (N = 9) were asked to perform kinesthetic motor imagery of both hands simultaneously cued with a clock indicating the initiation and termination of the action. Using electroencephalogram (EEG) signals, we built a decoder to detect the transition between event-related desynchronization and event-related synchronization. Features for this decoder were correlates of motor termination in the upper μ and β bands. The decoder reached an accuracy of 76.2% (N = 9), revealing the high robustness of our approach. More importantly, this paper shows that the decoding of motor termination has an intrinsic latency mainly due to the delayed appearance of its correlates. Because the latency was consistent and thus predictable, users were able to compensate it after training. Using our decoding system, BMI users were able to adapt their behavior and modulate their sensorimotor rhythm to stop the device (clock) accurately on time. These results show the importance of closed-loop evaluations of BMI decoders and open new possibilities for BMI control using decoding of movement termination.

Net-in-AI: A Computing-Power Networking Framework with Adaptability, Flexibility, and Profitability for Ubiquitous AI

Along with the unprecedented development of artificial intelligence (AI), a considerable number of intelligent applications are universally recognized to significantly facilitate the evolution of anthropogenic activities. The abundant AI computing power is one of the main pillars to fuel the booming of ubiquitous AI applications. As the computing power proliferates to a multitude of network edges, even end devices, the networking function bridges the gap, on the one hand, among ends-edges-clouds, on the other hand, between the multiple AI computing power and the heterogeneous AI requirements. The emerging new opportunities have spawned the deep integration between computing and networking. However, the complete development of the integrated system is under-addressed, including adaptability, flexibility, and profitability. In this article, we propose a computing-power networking framework for ubiquitous AI by establishing Networking in AI computing-power pool, denoted as Net-in-AI. We design the framework to enable the adaptability for computing-power users, the flexibility for networking, and the profitability for computing-power providers. We then formulate a computing-networking resource allocation problem, with the joint perspective of these three aspects. Experimental results prove the superior performance of the proposed framework in comparison to the current popular schemes.

Patient Interaction Phenotypes With an Automated Remote Hypertension Monitoring Program and Their Association With Blood Pressure Control: Observational Study.

BackgroundAutomated texting platforms have emerged as a tool to facilitate communication between patients and health care providers with variable effects on achieving target blood pressure (BP). Understanding differences in the way patients interact with these communication platforms can inform their use and design for hypertension management.ObjectiveOur primary aim was to explore the unique phenotypes of patient interactions with an automated text messaging platform for BP monitoring. Our secondary aim was to estimate associations between interaction phenotypes and BP control.MethodsThis study was a secondary analysis of data from a randomized controlled trial for adults with poorly controlled hypertension. A total of 201 patients with established primary care were assigned to the automated texting platform; messages exchanged throughout the 4-month program were analyzed. We used the k-means clustering algorithm to characterize two different interaction phenotypes: program conformity and engagement style. First, we identified unique clusters signifying differences in program conformity based on the frequency over time of error alerts, which were generated to patients when they deviated from the requested text message format (eg, ###/## for BP). Second, we explored overall engagement styles, defined by error alerts and responsiveness to text prompts, unprompted messages, and word count averages. Finally, we applied the chi-square test to identify associations between each interaction phenotype and achieving the target BP.ResultsWe observed 3 categories of program conformity based on their frequency of error alerts: those who immediately and consistently submitted texts without system errors (perfect users, 51/201), those who did so after an initial learning period (adaptive users, 66/201), and those who consistently submitted messages generating errors to the platform (nonadaptive users, 38/201). Next, we observed 3 categories of engagement style: the enthusiast, who tended to submit unprompted messages with high word counts (17/155); the student, who inconsistently engaged (35/155); and the minimalist, who engaged only when prompted (103/155). Of all 6 phenotypes, we observed a statistically significant association between patients demonstrating the minimalist communication style (high adherence, few unprompted messages, limited information sharing) and achieving target BP (P<.001).ConclusionsWe identified unique interaction phenotypes among patients engaging with an automated text message platform for remote BP monitoring. Only the minimalist communication style was associated with achieving target BP. Identifying and understanding interaction phenotypes may be useful for tailoring future automated texting interactions and designing future interventions to achieve better BP control.

Adaptive Elements in Internet-Delivered Psychological Treatment Systems: Systematic Review.

BackgroundInternet-delivered psychological treatments (IDPTs) are built on evidence-based psychological treatment models, such as cognitive behavioral therapy, and are adjusted for internet use. The use of internet technologies has the potential to increase access to evidence-based mental health services for a larger proportion of the population with the use of fewer resources. However, despite extensive evidence that internet interventions can be effective in the treatment of mental health disorders, user adherence to such internet intervention is suboptimal.ObjectiveThis review aimed to (1) inspect and identify the adaptive elements of IDPT for mental health disorders, (2) examine how system adaptation influences the efficacy of IDPT on mental health treatments, (3) identify the information architecture, adaptive dimensions, and strategies for implementing these interventions for mental illness, and (4) use the findings to create a conceptual framework that provides better user adherence and adaptiveness in IDPT for mental health issues.MethodsThe review followed the guidelines from Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The research databases Medline (PubMed), ACM Digital Library, PsycINFO, CINAHL, and Cochrane were searched for studies dating from January 2000 to January 2020. Based on predetermined selection criteria, data from eligible studies were analyzed.ResultsA total of 3341 studies were initially identified based on the inclusion criteria. Following a review of the title, abstract, and full text, 31 studies that fulfilled the inclusion criteria were selected, most of which described attempts to tailor interventions for mental health disorders. The most common adaptive elements were feedback messages to patients from therapists and intervention content. However, how these elements contribute to the efficacy of IDPT in mental health were not reported. The most common information architecture used by studies was tunnel-based, although a number of studies did not report the choice of information architecture used. Rule-based strategies were the most common adaptive strategies used by these studies. All of the studies were broadly grouped into two adaptive dimensions based on user preferences or using performance measures, such as psychometric tests.Conclusions Several studies suggest that adaptive IDPT has the potential to enhance intervention outcomes and increase user adherence. There is a lack of studies reporting design elements, adaptive elements, and adaptive strategies in IDPT systems. Hence, focused research on adaptive IDPT systems and clinical trials to assess their effectiveness are needed.

A Systematic Review of Personalized Collaborative Systems

Personalization, aiming at supporting users individually, according to their individual needs and prerequisites, has been discussed in a number of domains including learning, search, or information retrieval. In the field of human–computer interaction, personalization also bears high potential as users might exhibit varying and strongly individual preferences and abilities related to interaction. For instance, there is a good amount of work on personalized or adaptive user interfaces (also under the notion of intelligent user interfaces). Personalized human–computer interaction, however, does not only subsume approaches to support the individual user, it also bears high potential if applied to collaborative settings, for example, through supporting the individuals in a group as well as the group itself (considering all of its special dynamics). In collaborative settings (remote or co-located), there generally is a number of additional challenges related to human-to-human collaboration in a group, such as group communication, awareness or territoriality, device or software tool selection, or selection of collaborators. Personalized Collaborative Systems thus attempt to tackle many of these challenges. For instance, there are collaborative systems that recommend tools, content, or team constellations. Such systems have been suggested in different domains and different collaborative settings and contexts. In most cases, these systems explicitly focus on a certain aspect of personalized collaboration support (such as team composition). This article provides a broader, concise overview of existing approaches to Personalized Collaborative Systems based on a systematic literature review considering the ACM Digital Library.

Model-driven engineering and usability evaluation of self-adaptive user interfaces

Enes Yigitbas is a Senior Researcher at Paderborn University. In 2019, he got his doctoral degree in Computer Science under the advisory of Prof. Dr. Gregor Engels. During his doctorate, his research work was linked to various industrial projects in the Software Innovation Campus Paderborn (SICP). In this context, he was significantly involved in the implementation and management of various projects related to model-driven and human-centered software engineering, self-adaptive software systems, and human-computer interaction. His work on engineering adaptive user interfaces received a late-breaking results award at the ACM SIGCHI Symposium on Engineering Interactive Computing Systems. In addition to project and teaching activities, he is actively involved in various committee activities as well as serves as a reviewer for various international conferences and journals in the areas of Software Engineering and HCI. In his thesis, supervised by Prof. Dr. Gregor Engels (Paderborn University), he focused on the engineering and usability evaluation of self-adaptive user interfaces. Self-adaptive user interfaces (SAUIs) have been promoted as a solution for context variability due to their ability to automatically detect context changes and adapt to the current context-of-use at runtime. In the course of his thesis work, he developed a model-driven engineering approach which supports modeling, transformation, and execution of self-adaptive user interfaces. Furthermore, a novel on-the-fly usability testing solution for SAUIs has been introduced which allows to evaluate the end-user satisfaction of SAUIs by combining context monitoring together with collection of instant user feedback.

Accented visualization application in interactive manuals for technical training and support

The capabilities of modern information and communication devices (tablets, glasses, helmets, etc.) that provide user interfaces with Augmented Reality (AR) are successfully used in education and maintenance support nowadays. The main benefits are concerned with the possibility to display the required information in the context of operations performed by the user. Technically it is implemented in the form of textual hints or visual 3D objects with highly realistic appearance and behavior. New features prompt new challenges: the user can be easily overloaded with lots of information, and some real objects that require user’s attention can be overlapped by virtual ones in one scene. In order to solve this problem and improve the usability and effectiveness of AR devices application in maintenance and education it is proposed to build adaptive user interfaces that consider the context and focus of the user. From information technology perspective this approach forms a promising area of application of high-potential technologies including machine learning and computer vision. In this paper there is presented an example of AR implementation as a part of an interactive manual, which provides adaptation and personalization based on the concept of accented visualization. Identification of objects and their classification is implemented using the artificial neural network.

Beyond QoE: Diversity Adaptation in Video Streaming at the Edge

Adaptive bitrate (ABR) algorithms are critical techniques for high quality-of-experience (QoE) Internet video delivery. Early ABR algorithms conducting the overall QoE function of fixed parameters are limited by the fact that the QoE of end-users are diverse such that the video bitrate is often chosen in a misleading way. State-of-the-art ABR algorithms like MPC and Pensieve utilize offline modeling techniques and result in performance degradation for online QoE diversity adaptation. To address this issue, we propose Elephanta, an online ABR algorithm for edge users, which incorporates user QoE perception interface and adaptation algorithm with flexible parameters. In order to avoid overhead from updating parameters online, we model video streaming as a renewal system and formulate the specific QoE function into flexible formats by setting constraints on corresponding QoE metrics. To validate parameter settings, we emulate Elephanta under 1500 throughput traces, including FCC broadband, $3G$ HSDPA data set from the Internet, as well as the $4G$ /LTE data set we collect. Evaluation results show that Elephanta achieves QoE improvement of 7% over MPC and 3% over Pensieve under QoE diversity in part because of its superior adaptability to QoE diversity. We implemented Elephanta in dash.js at the client side for subjective experiments. We observed the diverse QoE preferences across users and 19/21 users (strongly) agree that Elephanta is responsive to parameter changes while watching videos.

Arbel mediko baten erabileraren azterketa: nolako elkarrekintza halako portaera-bisuala

Osasun-arlorako softwarean, beste esparru askotan bezala, erabilgarritasuna erronka handia da; batetik, datuak ugariak eta konplexuak direlako eta, bestetik, erabilera-testuingurua kritikoa delako. Jakina da langile klinikoek informazio kopuru egokia eskatzen dutela beren zereginak aurrera eramateko, eta, zentzu horretan, erabiltzaile-interfaze moldagarriak oso baliagarriak izan daitezke informazio-behar horiek asetzeko nahiz informazio-gainkargaren arazoari heltzeko. Erabiltzaile-interfazerako egokitzapenak inplementatu aurretik, baina, aldakorrak diren informazio-behar horiek identifikatu eta lehenetsi egin behar dira. Begi-arakatzaileak lagungarriak izan daitezke zeregin hauek erdiesteko, erabiltzaileen portaera bisuala antzeman baitezakete, zeina interesaren adierazle den. Zoritxarrez, arakatzaile horiek sistema hedatu batean erabiltzeak azpiegitura konplexuegia eskatzen du. Ekarpen honetan aztertu dugu ea erabiltzaileen portaera bisuala inferi ote daitekeen erabiltzaile horiek botiken segurtasunari lotutako arbel batean izandako elkarrekintzaren datuetatik abiatuz. Emaitzek aditzera eman dute bi alderdiak, hots, saguaren bidezko elkarrekintza eta portaera bisuala (karga kognitiboa), lotuta daudela; ezaugarri hauen arabera neurtuak, hurrenez hurren: saguaren ondoz ondoko bi kliken arteko denbora-tarteak eta sagu-pausatzeak, batetik, eta, bestetik, begiradaren finkapenaren iraupenak. Lanak eztabaidatzen du zer eragin izan dezakeen aurkikuntza horrek arbel medikoen egokitzapenen diseinuan.

Interactive Geoinformation Three-Dimensional Model of a Landscape Park Using Geoinformatics Tools

The relevance of work is due to the lack of free three-dimensional models of landscape parks and the urgent need to create such models for informing and barrier-free adaptation of users. The current state of cartographic support of landscape parks is low and does not meet users, management, and employees of parks. The object of research is the territory of the multicomplex. The purpose of the research is to develop a three-dimensional model of the regional landscape park for informing and barrier-free adaptation of residents and guests of the city using geoinformatics tools. The methods used in this research are general scientific (system analysis, modeling, higher mathematics) and special methods of mathematical and geoinformation analysis. The work uses the ArcGIS and SketchUp software platforms to create, manage, integrate, analyze, and distribute spatial data. Methods used: geodesy, topography, geoinformatics. The results obtained in the course of the work are of practical importance, are real and tested by the management of the regional landscape park. An interactive three-dimensional geoinformation model implemented in the ArcScene software environment will provide an opportunity further to manage the territory of the regional landscape park. An interactive geoinformation three-dimensional model ensured a 12% increase in the level of informational and barrier-free adaptation of residents and guests on the territory of the multicomplex. The prospects for further research on the creation of a mobile application for park visitors with the ability to search for the nearest bus departure point at their location are determined.

Adaptive User Experience and Empathetic HMI Concepts

Adaptive User Experience and Empathetic HMI Concepts

Adaptive setpoint temperatures to reduce the risk of energy poverty? A local case study in Seville

The reduction of energy poverty is among the main current challenges. One of the recent approaches is based on the reduction of the energy consumption through the climate adaptability of users. This research analyses the possibility of using adaptive setpoint temperatures to reduce the risk of energy poverty. A total of 6528 cases are considered in the south of Spain in 2015 and 2016 with actual data of temperature, hourly prices from the Voluntary Price for the Small Consumer, and the mean household incomes in both years. The energy consumption and expense are compared to both the static setpoints established by the Spanish Technical Building Code and the adaptive setpoints based on EN 16798-1:2019. In the annual calculation, by using both static and adaptive setpoints, the results show that the situation of energy poverty would only affect the family units belonging to the first decile of incomes. However, a monthly analysis identifies that the coldest or warmest months influence more deciles: for example, January 2015 affected until decile 8. The results also show that adaptive setpoints could reduce the risk of energy poverty in most cases, being more significant in Categories II and III from EN 16798-1:2019, in which this risk is reduced in all months of the year and in all deciles. This study aims to throw light on the use of HVAC systems according to the adaptation of users to reduce monthly the risk of energy poverty.

Detecting and Responding to Information Overload With an Adaptive User Interface.

The objective of this study was to develop and evaluate an adaptive user interface that could detect states of operator information overload and calibrate the amount of information on the screen. Machine learning can detect changes in operating context and trigger adaptive user interfaces (AUIs) to accommodate those changes. Operator attentional state represents a promising aspect of operating context for triggering AUIs. Behavioral rather than physiological indices can be used to infer operator attentional state. In Experiment 1, a network analysis task sought to induce states of information overload relative to a baseline. Streams of interaction data were taken from these two states and used to train machine learning classifiers. We implemented these classifiers in Experiment 2 to drive an AUI that automatically calibrated the amount of information displayed to operators. Experiment 1 successfully induced information overload in participants, resulting in lower accuracy, slower completion time, and higher workload. A series of machine learning classifiers detected states of information overload significantly above chance level. Experiment 2 identified four clusters of users who responded significantly differently to the AUIs. The AUIs benefited performance, completion time, and workload in three clusters. Behavioral indices can successfully detect states of information overload and be used to effectively drive an AUI for some user groups. The success of AUIs may be contingent on characteristics of the user group. This research applies to domains seeking real-time assessments of user attentional or psychological state.

Wearable multichannel haptic device for encoding proprioception in the upper limb

Objective. We present the design, implementation, and evaluation of a wearable multichannel haptic system. The device is a wireless closed-loop armband driven by surface electromyography (EMG) and provides sensory feedback encoding proprioception. The study is motivated by restoring proprioception information in upper limb prostheses. Approach. The armband comprises eight vibrotactile actuators that generate distributed patterns of mechanical waves around the limb to stimulate perception and to transfer proportional information on the arm motion. An experimental study was conducted to assess: the sensory threshold in eight locations around the forearm, the user adaptation to the sensation provided by the device, the user performance in discriminating multiple stimulation levels, and the device performance in coding proprioception using four spatial patterns of stimulation. Eight able-bodied individuals performed reaching tasks by controlling a cursor with an EMG interface in a virtual environment. Vibrotactile patterns were tested with and without visual information on the cursor position with the addition of a random rotation of the reference control system to disturb the natural control and proprioception. Main results. The sensation threshold depended on the actuator position and increased over time. The maximum resolution for stimuli discrimination was four. Using this resolution, four patterns of vibrotactile activation with different spatial and magnitude properties were generated to evaluate their performance in enhancing proprioception. The optimal vibration pattern varied among the participants. When the feedback was used in closed-loop control with the EMG interface, the task success rate, completion time, execution efficiency, and average target-cursor distance improved for the optimal stimulation pattern compared to the condition without visual or haptic information on the cursor position. Significance. The results indicate that the vibrotactile device enhanced the participants’ perceptual ability, suggesting that the proposed closed-loop system has the potential to code proprioception and enhance user performance in the presence of perceptual perturbation.

EmotionSense

With the recent surge of smart wearable devices, it is possible to obtain the physiological and behavioral data of human beings in a more convenient and non-invasive manner. Based on such data, researchers have developed a variety of systems or applications to recognize and understand human behaviors, including both physical activities (e.g., gestures) and mental states (e.g., emotions). Specifically, it has been proved that different emotions can cause different changes in physiological parameters. However, other factors, such as activities, may also impact one’s physiological parameters. To accurately recognize emotions, we need not only explore the physiological data but also the behavioral data. To this end, we propose an adaptive emotion recognition system by exploring a sensor-enriched wearable smart watch. First, an activity identification method is developed to distinguish different activity scenes (e.g., sitting, walking, and running) by using the accelerometer sensor. Based on the identified activity scenes, an adaptive emotion recognition method is proposed by leveraging multi-mode sensory data (including blood volume pulse, electrodermal activity, and skin temperature). Specifically, we extract fine-grained features to characterize different emotions. Finally, the adaptive user emotion recognition model is constructed and verified by experiments. An accuracy of 74.3% for 30 participants demonstrates that the proposed system can recognize human emotions effectively.

Bring Your Own Device (BYOD) as reversed IT adoption: Insights into managers’ coping strategies

The adoption of Bring Your Own Device (BYOD), initiated by employees, refers to the provision and use of personal mobile devices and applications for both private and business purposes. This bottom-up phenomenon, not initiated by managers, corresponds to a reversed IT adoption logic that simultaneously entails business opportunities and threats. Managers are thus confronted with this unchosen BYOD usage by employees and consequently adopt different coping strategies. This research aims to investigate the adaptation strategies embraced by managers to cope with the BYOD phenomenon. To this end, we operationalized the coping model of user adaptation (CMUA) in the organizational decision-making context to conduct a survey addressing 337 top managers. Our main results indicate that the impact of the CMUA constructs varies according to the period (pre- or post-implementation). The coping strategies differ between those who have already implemented measures to regulate BYOD usage and those who have not. We contribute to theory by integrating the perception of BYOD-related opportunities and threats and by shedding light on the decisional processes in the adoption of coping strategies. The managerial contributions of this research correspond to the improved protection of corporate information and the maximization of BYOD-related benefits.

Geniş İç Açıklıklardaki Yüksek Düzeyli Doğal Aydınlatma Sorununa Parametrik bir Çözümleme: İZÜ Kütüphanesi İç Mekân Gölgeleme Enstalasyonu

Mimari yapılarda ışık kullanıcının mekana uyum sağlaması, psikolojik konforu ve mekanı kullanım performansı ile ilgili önemli bir bileşendir. Okuma ve kıymetli eserlerin korunması gibi işlevleri barındıran kütüphane yapılarında ise ışık, yapı karakteristiğinin kurgulanmasında oldukça aktif rol oynar. Kütüphane yapılarındaki aydınlatma düzeyleri ve özellikle gün ışığının kontrollü şekilde mekana alınması da dikkat edilmesi gereken parametrelerdendir. Bu makale, İstanbul Zaim Üniversitesi’nin Halkalı kampüsünde bulunan kütüphanesi için geliştirilen bir iç mekan gölgeleme elemanının tasarım ve üretim süreçlerini özetlemektedir. Kütüphanenin toplamda 500 m2’lik galeri boşluklarında, ortam konforunu yüksek düzeyli doğal aydınlatma sonucu etkileyen bir tasarım problemi tespit edilmiştir. Matematiksel verilere dayandırılan bu probleme çözüm olarak, parametrik tasarım ilke ve yaklaşımlarıyla çeşitli tasarımlar geliştirilmiş ve alternatiflerden biri seçilerek uygulanmıştır. Çalışmanın başında; düşük maliyet ve iş gücü, mekan estetiği ve galeri etkisinin korunması, basit ve güvenli yapı malzemeleri tercih edilmesi gibi birtakım kriterler belirlenmiştir. Uygulanan tasarım oldukça pratik, ucuz ve özgün bir yöntemle bu gibi kriterleri karşılayacak şekilde geliştirilmiştir. Ortam konforuna sağlanan katkının sayısal verilerle desteklendiği çalışmada, benzer geniş iç açıklıkların gölgelenebilmesi için de mimarlığa pratik bir yöntem önerilmiştir. Çalışmanın sonunda ortaya çıkan parametrik örtü enstalasyonun özgün tasarım ve üretim aşamaları ve ortam kalitesine gölgeleme açısından yaptığı katkı bu makalenin üzerinde durduğu konulardır.

A Framework for User Adaptation and Profiling for Social Robotics in Rehabilitation.

Physical rehabilitation therapies for children present a challenge, and its success—the improvement of the patient’s condition—depends on many factors, such as the patient’s attitude and motivation, the correct execution of the exercises prescribed by the specialist or his progressive recovery during the therapy. With the aim to increase the benefits of these therapies, social humanoid robots with a friendly aspect represent a promising tool not only to boost the interaction with the pediatric patient, but also to assist physicians in their work. To achieve both goals, it is essential to monitor in detail the patient’s condition, trying to generate user profile models which enhance the feedback with both the system and the specialist. This paper describes how the project NAOTherapist—a robotic architecture for rehabilitation with social robots—has been upgraded in order to include a monitoring system able to generate user profile models through the interaction with the patient, performing user-adapted therapies. Furthermore, the system has been improved by integrating a machine learning algorithm which recognizes the pose adopted by the patient and by adding a clinical reports generation system based on the QUEST metric.