Search Results (68)

As a result of population ageing, rehabilitation assistive products for older adults increasingly need to meet long-term user requirements in terms of usability, comfort, and interactive experience. Although existing hand rehabilitation robots (HRRs) are capable of supporting hand training tasks, they still exhibit deficiencies in experience-oriented design, which in turn affect user acceptability and continued use. This study aimed to identify and prioritize user requirements for HRRs and, based on their relative importance, formulate emotional design strategies to inform future UX-oriented development. User interviews, the Kano questionnaire, and an adapted quality function deployment (QFD) requirement-prioritization framework were employed for the systematic investigation. The results revealed a clear priority structure. Personalized and adaptive training (H8) ranked first, followed by function–form integration (H3), continuous functional updates (H15), ergonomic wearing comfort (H4), and voice guidance (H9). Clear and readable screen content (H5), timely feedback (H11), positive emotional motivation (H17), and mobile connectivity (H13) also received relatively high priorities. These findings provide a quantifiable basis for design decision-making and subsequent prototype development. The study identifies user priorities and proposes design strategies; it does not experimentally demonstrate improvements in UX, adherence, or rehabilitation outcomes. Full article

Social robots are systems designed to assist people across different fields. During their operation, they have to interact with people with different characteristics and necessities. Consequently, correctly recognising the user interacting with the robot facilitates the generation of a personalised experience that satisfies the user’s needs. In robotics, user recognition is typically based on face recognition from image processing and datasets that require retraining the network to include new users. However, some robots, such as pet-like companions, often lack a camera due to reduced dimensions, limited computational resources, or privacy constraints. Additionally, robots can occasionally encounter new users, requiring online recognition to provide a personalised interaction experience. To address these limitations, this article presents a user recognition system based on voice biometrics and dynamic clustering for adaptive social robots. We evaluate a set of open-source models for voice biometric extraction using different clustering algorithms to identify the best combination for our application. The resulting system is implemented in a pet-like robot companion that is used for the affective support of older adults, demonstrating its capacities in a real-world scenario. The system achieves more than $73\%$ accuracy in recognising users who had previously spoken to the robot and more than $71\%$ success in recognising new users who had not previously interacted with the robot and creating a personal profile for them. However, the system still detects noise, especially when the speaker has never interacted with the robot. Full article

Background: Youth with disabilities remain among the most overlooked groups in global sexual and reproductive health and rights (SRHR) discourses, including in sub-Saharan Africa. Yet, their SRHR needs are often ignored. This reflexive article aims to illuminate and recenter the experiences and perspectives of youth with disabilities living in Gulu City and Gulu District, Northern Uganda, exploring what matters to them regarding SRHR and their broader life aspirations. Methods: We adopted a qualitative, reflexive and participatory approach. Data were collected among six Ugandan young co-researchers with different disabilities (physical, visual, hearing, and albinism), who interacted with two Ugandan research assistants and a Canadian researcher involved in a larger SRHR research project. They engaged in in-person and virtual WhatsApp and Microsoft Teams exchanges over weeks, with the support of three Ugandan Sign Language interpreters. We thematically analyzed data, informed by the Intersectionality-based Policy Analysis and Structural Health Vulnerabilities and Agency frameworks. Results: Our analysis revealed four main findings: (1) the persistent feeling of social discrimination, stigma, and exclusion, including from parents, (2) inaccessible SRHR information and services, and knowledge gaps, (3) gender- and disability-based violence, and (4) youth with disabilities’ aspirations for SRHR and in life. Conclusions: The voices of youth with disabilities in Gulu underscore the value of disability equity-focused research. They reminded us that they are intelligent, capable, and thoughtful citizens with agency whose SRHR and broader well-being must be acknowledged and respected. Their perspectives carry critical implications for SRHR programming, policy, and research. Full article

Artificial intelligence (AI) is transforming customer experience management (CXM) by enabling real-time, data-driven, and personalized interactions across digital touchpoints, including chatbots, voice assistants, generative AI, and immersive platforms. This study presents a PRISMA-based systematic literature review of 59 peer-reviewed studies published between 2021 and 2026, examining how AI-enabled personalization, privacy concerns, and customer value interact within AI-mediated customer experiences. Drawing on the Personalization–Privacy–Value (PPV) framework, the review synthesizes evidence on how AI-driven personalization enhances utilitarian, hedonic, experiential, relational, and emotional value, thereby strengthening satisfaction, engagement, loyalty, and behavioral intentions. At the same time, the findings reveal persistent tensions, as privacy concerns, perceived surveillance, algorithmic bias, and contextual moderators—including generational differences, cultural expectations, and technological literacy—frequently constrain value creation and erode trust. The review highlights that personalization benefits are highly contingent on transparency, perceived control, and ethical alignment, rather than personalization intensity alone. The study contributes by integrating ethical AI considerations into CXM research and clarifying conditions under which AI-enabled personalization leads to value creation versus value destruction. Managerially, the findings underscore the importance of ethical governance, transparent data practices, and customer-centered AI design to sustain trust and long-term customer relationships. Future research should prioritize longitudinal analyses of trust development, demographic heterogeneity, and cross-sector comparisons of AI governance as AI technologies become increasingly embedded in service ecosystems. Full article

Smart virtual reality (VR) systems are becoming central to media production education, where immersive practice, real-time feedback, and hands-on simulation are essential. This review synthesizes the integration of artificial intelligence (AI) into human-centered, interactive VR learning for television and media production. Searches in Scopus, Web of Science, IEEE Xplore, ACM Digital Library, and SpringerLink (2013–2024) identified 790 records; following PRISMA screening, 94 studies met the inclusion criteria and were synthesized using a systematic scoping review approach. Across this corpus, common AI components include learner modeling, adaptive task sequencing (e.g., RL-based orchestration), affect sensing (vision, speech, and biosignals), multimodal interaction (gesture, gaze, voice, haptics), and growing use of LLM/NLP assistants. Reported benefits span personalized learning trajectories, high-fidelity simulation of studio workflows, and more responsive feedback loops that support creative, technical, and cognitive competencies. Evaluation typically covers usability and presence, workload and affect, collaboration, and scenario-based learning outcomes, leveraging interaction logs, eye tracking, and biofeedback. Persistent challenges include latency and synchronization under multimodal sensing, data governance and privacy for biometric/affective signals, limited transparency/interpretability of AI feedback, and heterogeneous evaluation protocols that impede cross-system comparison. We highlight essential human-centered design principles—teacher-in-the-loop orchestration, timely and explainable feedback, and ethical data governance—and outline a research agenda to support standardized evaluation and scalable adoption of smart VR education in the creative industries. Full article

People with Down Syndrome often experience more barriers to achieving a good quality of life compared to people without disabilities. A lot of the existing research has focused on the views of parents and professionals, rather than directly including the voices and perspectives of people with Down Syndrome themselves. We wanted to find out how this might be done. At the 2024 World Down Syndrome Conference, over 140 adults with Down Syndrome came together at a one-day Forum to talk about their lives—aspects that are going well and what could be better. The goal was to hear directly from them. This article explains how the Forum was run so that others with Down Syndrome can use a similar process. We describe how Artificial Intelligence (AI) was used to assist the authors in organising and sharing the information from participants, such as grouping what people said into different themes and helping to create plain language reports. This process worked. Eight key themes were found that could help people to have a good life, such as having good relationships with family and friends; having a job; making personal choices; and being respected and included. The list was longer than previously reported in other studies. The Forum gave valuable insights and helped us think of new ideas for supporting people with Down Syndrome to speak up for themselves. Used thoughtfully, AI (Artificial Intelligence) could be a helpful tool in the future to help these people share their experiences and needs. More research is needed to understand how people with Down Syndrome can be more involved in making changes through advocacy projects where they take an active role. Full article

Virtual assistants in academic environments often lack comprehensive multimodal integration and specialized role-based architecture. This paper presents NAIA (Nimble Artificial Intelligence Assistant), a robust artificial intelligence framework designed for multi-role virtual academic assistance through a modular monolithic approach. The system integrates Large Language Models (LLMs), Computer Vision, voice processing, and animated digital avatars within five specialized roles: researcher, receptionist, personal skills trainer, personal assistant, and university guide. NAIA’s architecture implements simultaneous voice, vision, and text processing through a three-model LLM system for optimized response quality, Redis-based conversation state management for context-aware interactions, and strategic third-party service integration with OpenAI, Backblaze B2, and SerpAPI. The framework seamlessly connects with the institutional ecosystem through Microsoft Graph API integration, while the frontend delivers immersive experiences via 3D avatar rendering using Ready Player Me and Mixamo. System effectiveness is evaluated through a comprehensive mixed-methods approach involving 30 participants from Universidad del Norte, employing Technology Acceptance Model (TAM2/TAM3) constructs and System Usability Scale (SUS) assessments. Results demonstrate strong user acceptance: 93.3% consider NAIA useful overall, 93.3% find it easy to use and learn, 100% intend to continue using and recommend it, and 90% report confident independent operation. Qualitative analysis reveals high satisfaction with role specialization, intuitive interface design, and institutional integration. The comparative analysis positions NAIA’s distinctive contributions through its synthesis of institutional knowledge integration with enhanced multimodal capabilities and specialized role architecture, establishing a comprehensive framework for intelligent human-AI interaction in modern educational environments. Full article

Background: Although equine-assisted interventions (EAI) are gaining growing attention, their scientific evaluation among individuals with Alzheimer’s disease (AD) living in nursing homes remains limited. This study aimed to explore the lived experiences of an EAI program from the perspectives of the participants living with AD as well as their families and professional caregivers. Methods: Thirty non-directive interviews were conducted between June and July 2024 across several nursing homes in the Centre-Val de Loire region (France). The interviews were recorded, transcribed, and analyzed using thematic analysis. Results: Four main themes emerged from the analysis: (1) the experience with the horse, reflecting a unique relationship with the animal, the activities carried out, and perceived personality traits; (2) the environment of EAI sessions, offering a break from daily routines, encouraging contact with nature, and taking place in a setting specific to this type of intervention; (3) the implementation of the program within the institutional context, highlighting logistical aspects, environmental factors, and the adherence; (4) the effects of the intervention, including enhanced social interactions, memory stimulation, emotional engagement, and behavioral benefits. Conclusions: These findings provide insight into the multiple dimensions involved in an EAI program. By giving voice to both participants and their caregivers, this study emphasizes the value of qualitative approaches in deeply understanding the meaning and impact of these non-pharmacological interventions. Full article

This paper introduces a comprehensive approach to building natural-sounding Urdu Text-to-Speech (TTS) and voice cloning systems, addressing the lack of computational resources for Urdu. We developed a large-scale dataset of over 100 h of Urdu speech, carefully cleaned and phonetically aligned through an automated transcription pipeline to preserve linguistic accuracy. The dataset was then used to fine-tune Tacotron2, a neural network model originally trained for English, with modifications tailored to Urdu’s phonological and morphological features. To further enhance naturalness, we integrated voice cloning techniques that capture regional accents and produce personalized speech outputs. Model performance was evaluated through mean opinion score (MOS), word error rate (WER), and speaker similarity, showing substantial improvements compared to previous Urdu systems. The results demonstrate clear progress toward natural and intelligible Urdu speech synthesis, while also revealing challenges such as handling dialectal variation and preventing model overfitting. This work contributes an essential resource and methodology for advancing Urdu natural language processing (NLP), with promising applications in education, accessibility, entertainment, and assistive technologies. Full article

Background/Objective: The global acceleration of population aging presents profound challenges to the physical, psychological, and social well-being of older adults. As traditional exercise programs face limitations in accessibility, personalization, and sustained social support, there is a critical need for innovative, inclusive, and community-integrated digital movement solutions. This study aimed to develop and evaluate Movement Poomasi, a hybrid digital healthcare application designed to promote physical activity, improve digital accessibility, and strengthen social connectedness among older adults. Methods: From March 2023 to November 2023, Movement Poomasi was developed through an iterative user-centered design process involving domain experts in physical therapy and sports psychology. In this study, the term UI/UX—short for user interface and user experience—refers to the overall design and interaction framework of the application, encompassing visual layout, navigation flow, accessibility features, and user engagement optimization tailored to older adults’ sensory, cognitive, and motor characteristics. The application integrates adaptive exercise modules, senior-optimized UI/UX, voice-assisted navigation, and peer-interaction features to enable both home-based and in-person movement engagement. A two-phase usability validation was conducted. A 4-week pilot test with 15 older adults assessed the prototype, followed by a formal 6-week study with 50 participants (≥65 years), stratified by digital literacy and activity background. Quantitative metrics—movement completion rates, session duration, and engagement with social features—were analyzed alongside semi-structured interviews. Statistical analysis included ANOVA and regression to examine usability and engagement outcomes. The application has continued iterative testing and refinement until May 2025, and it is scheduled for re-launch under the name Wello! in August 2025. Results: Post-implementation UI refinements significantly increased navigation success rates (from 68% to 87%, p = 0.042). ANOVA revealed that movement selection and peer-interaction tasks posed greater cognitive load (p < 0.01). A strong positive correlation was found between digital literacy and task performance (r = 0.68, p < 0.05). Weekly participation increased by 38%, with 81% of participants reporting enhanced social connectedness through group challenges and hybrid peer-led meetups. Despite high satisfaction scores (mean 4.6 ± 0.4), usability challenges remained among low-literacy users, indicating the need for further interface simplification. Conclusions: The findings underscore the potential of hybrid digital platforms tailored to older adults’ physical, cognitive, and social needs. Movement Poomasi demonstrates scalable feasibility and contributes to reducing the digital divide while fostering active aging. Future directions include AI-assisted onboarding, adaptive tutorials, and expanded integration with community care ecosystems to enhance long-term engagement and inclusivity. Full article

The absence of parental presence has a direct impact on the emotional stability and social routines of children, especially during extended periods of separation from their family environment, as in the case of daycare centers, hospitals, or when they remain alone at home. At the same time, the technology currently available to provide emotional support in these contexts remains limited. In response to the growing need for emotional support and companionship in child care, this project proposes the development of a multi-platform software architecture based on artificial intelligence (AI), designed to be integrated into humanoid robots that assist children between the ages of 6 and 14. The system enables daily verbal and non-verbal interactions intended to foster a sense of presence and personalized connection through conversations, games, and empathetic gestures. Built on the Robot Operating System (ROS), the software incorporates modular components for voice command processing, real-time facial expression generation, and joint movement control. These modules allow the robot to hold natural conversations, display dynamic facial expressions on its LCD (Liquid Crystal Display) screen, and synchronize gestures with spoken responses. Additionally, a graphical interface enhances the coherence between dialogue and movement, thereby improving the quality of human–robot interaction. Initial evaluations conducted in controlled environments assessed the system’s fluency, responsiveness, and expressive behavior. Subsequently, it was implemented in a pediatric hospital in Guayaquil, Ecuador, where it accompanied children during their recovery. It was observed that this type of artificial intelligence-based software, can significantly enhance the experience of children, opening promising opportunities for its application in clinical, educational, recreational, and other child-centered settings. Full article

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disorder that progressively impairs motor and communication abilities. Globally, the prevalence of ALS was estimated at approximately 222,800 cases in 2015 and is projected to increase by nearly 70% to 376,700 cases by 2040, primarily driven by demographic shifts in aging populations, and the lifetime risk of developing ALS is 1 in 350–420. Despite international advancements in assistive technologies, a recent national survey in Saudi Arabia revealed that 100% of ALS care providers lack access to eye-tracking communication tools, and 92% reported communication aids as inconsistently available. While assistive technologies such as speech-generating devices and gaze-based control systems have made strides in recent decades, they primarily support English speakers, leaving Arabic-speaking ALS patients underserved. This paper presents SOUTY, a cost-effective, mobile-based application that empowers ALS patients to communicate using gaze-controlled interfaces combined with a text-to-speech (TTS) feature in Arabic language, which is one of the five most widely spoken languages in the world. SOUTY (i.e., “my voice”) utilizes a personalized, pre-recorded voice bank of the ALS patient and integrated eye-tracking technology to support the formation and vocalization of custom phrases in Arabic. This study describes the full development life cycle of SOUTY from conceptualization and requirements gathering to system architecture, implementation, evaluation, and refinement. Validation included expert interviews with Human–Computer Interaction (HCI) expertise and speech pathology specialty, as well as a public survey assessing awareness and technological readiness. The results support SOUTY as a culturally and linguistically relevant innovation that enhances autonomy and quality of life for Arabic-speaking ALS patients. This approach may serve as a replicable model for developing inclusive Augmentative and Alternative Communication (AAC) tools in other underrepresented languages. The system achieved 100% task completion during internal walkthroughs, with mean phrase selection times under 5 s and audio playback latency below 0.3 s. Full article

Background/Objectives: Artificial intelligence (AI) offers opportunities to enhance healthcare accessibility for people with disabilities (PwDs). However, their application in Saudi Arabia remains limited. This study explores PwDs’ experiences with AI technologies within the Kingdom’s Vision 2030 digital health framework to inform inclusive healthcare innovation strategies. Methods: Semi-structured interviews were conducted with nine PwDs across Riyadh, Al-Jouf, and the Northern Border region between January and February 2025. Participants used various AI-enabled technologies, including smart home assistants, mobile health applications, communication aids, and automated scheduling systems. Thematic analysis following Braun and Clarke’s six-phase framework was employed to identify key themes and patterns. Results: Four major themes emerged: (1) accessibility and usability challenges, including voice recognition difficulties and interface barriers; (2) personalization and autonomy through AI-assisted daily living tasks and medication management; (3) technological barriers such as connectivity issues and maintenance gaps; and (4) psychological acceptance influenced by family support and cultural integration. Participants noted infrastructure gaps in rural areas, financial constraints, limited disability-specific design, and digital literacy barriers while expressing optimism regarding AI’s potential to enhance independence and health outcomes. Conclusions: Realizing the benefits of AI for disability healthcare in Saudi Arabia requires culturally adapted designs, improved infrastructure investment in rural regions, inclusive policymaking, and targeted digital literacy programs. These findings support inclusive healthcare innovation aligned with Saudi Vision 2030 goals and provide evidence-based recommendations for implementing AI healthcare technologies for PwDs in similar cultural contexts. Full article

With the rapid development of intelligent transportation systems, voice assistants are increasingly integrated into driving environments, providing an effective means to mitigate the risks of fatigued driving. This study explored drivers’ interaction preferences with voice assistants under different fatigue states and proposed a fatigue-state-based dialogue-awakening mechanism. Using Grounded Theory and the Stimulus–Organism–Response (SOR) framework, in-depth interviews were conducted with 25 drivers from diverse occupational backgrounds. To validate the qualitative findings, a driving simulation experiment was carried out to examine the effects of different voice interaction styles on driver fatigue arousal across various fatigue levels. Results indicated that heavily fatigued drivers preferred highly stimulating and interactive voice communication; mildly fatigued drivers tended toward gentle and socially supportive dialogue; while drivers in a non-fatigued state preferred minimal voice interference, activating voice assistance only when necessary. Significant occupational differences were also observed: long-haul truck drivers emphasized practicality and safety in voice assistants, taxi drivers favored voice interactions combining navigation and social content, and private car owners preferred personalized and emotional support. This study enriches the theoretical understanding of fatigue-sensitive voice interactions and provides practical guidance for the adaptive design of intelligent voice assistants, promoting their application in driving safety. Full article

Grounded in the existing literature on administrative burden and using a qualitative and community-engaged research approach, the research examined the administrative burden experienced in accessing disability, retirement, and survivor benefits from the Social Security Administration (SSA). The research team held in person and virtual focus groups and interviews with 40 adults with disabilities, older adults, and family members of people with disabilities who resided in rural areas of the U.S. State of New Hampshire in 2024. The qualitative analysis revealed that rural residents, regardless of type of SSA benefit receipt, were experiencing high levels of administrative burden in their interactions with the SSA and preferred to turn to in-person assistance at local SSA field offices (rather than phone, mail, or web-based service options) to address these concerns. Overall, people living in rural counties that do not have local SSA field offices voiced a distinct disadvantage in terms of knowing where to turn with questions about their benefits. A lack of ready and reliable access to information and advice led to endangering their own economic stability and to increased calls and visits to the SSA. Persons with stronger social networks were better able to overcome these barriers to services. Full article