Search Results (127)

The accelerating global population aging has brought increasing attention to the loneliness and emotional needs experienced by older adults due to shrinking social networks and the loss of relatives and friends, which significantly impair their quality of life and psychological well-being. In this context, companion robots powered by artificial intelligence are increasingly regarded as a scalable and sustainable form of emotional intervention that can address older people’s affective and social requirements. This study systematically reviews research trends in this field, analyzing the structure of emotional needs among older users and their acceptance mechanisms toward robot functionalities. First, a keyword co-occurrence analysis was conducted using VOSviewer on relevant literature published between 2000 and 2025 from the Web of Science database, revealing focal research topics and emerging trends. Subsequently, questionnaire surveys and in-depth interviews were carried out to identify emotional needs and functional preferences among elderly users. Findings indicate that the field is characterized by increasing interdisciplinary integration, with affective computing and naturalistic interaction becoming central concerns. Empirical results reveal significant differences in need structures across age groups: the oldest-old prioritize safety monitoring and daily assistance, whereas the young-old emphasize social interaction and developmental activities. Regarding emotional interaction, older adults generally prefer natural and non-intrusive expressive styles and exhibit reserved attitudes toward highly anthropomorphic designs. Key factors influencing acceptance include practicality, ease of use, privacy protection, and emotional warmth. The study concludes that effective companion robot design should be grounded in a nuanced understanding of the heterogeneous needs of the aging population, integrating functionality, interaction, and emotional value. Future development should emphasize adaptive and customizable capabilities, adopt natural yet restrained interaction strategies, and strengthen real-world cross-cultural and long-term evaluations. Full article

Speech emotion recognition (SER) has become increasingly important in areas such as healthcare, customer service, robotics, and human–computer interaction. The progress of this field depends not only on advances in algorithms but also on the databases that provide the training material for SER systems. These resources set the boundaries for how well models can generalize across speakers, contexts, and cultures. In this paper, we present a narrative review and comparative analysis of emotional speech corpora released up to mid-2025, bringing together both psychological and technical perspectives. Rather than following a systematic review protocol, our approach focuses on providing a critical synthesis of more than fifty corpora covering acted, elicited, and natural speech. We examine how these databases were collected, how emotions were annotated, their demographic diversity, and their ecological validity, while also acknowledging the limits of available documentation. Beyond description, we identify recurring strengths and weaknesses, highlight emerging gaps, and discuss recent usage patterns to offer researchers both a practical guide for dataset selection and a critical perspective on how corpus design continues to shape the development of robust and generalizable SER systems. Full article

Background: Endometriosis is a chronic, estrogen-dependent inflammatory disease affecting up to 10% of women of reproductive age. It substantially impacts quality of life (QoL) through pelvic pain, infertility, and psychological distress. Increasing attention has been directed toward patient-reported outcomes and validated QoL instruments, which are essential for understanding the burden of disease and guiding individualized management. Materials and Methods: We performed a narrative review of the literature published in the last five years in PubMed, Scopus, Web of Science, and Cochrane Library, focusing on validated QoL instruments, fertility indices, and clinical outcomes after minimally invasive surgery (MIS) for deep infiltrating endometriosis (DIE). Discussions: The most widely used QoL instruments are the Endometriosis Health Profile-30 (EHP-30), Short Form-36 (SF-36), and EQ-5D, each providing multidimensional evaluation across physical, psychological, and social domains. Fertility-related prognosis is assessed with the Endometriosis Fertility Index (EFI), while staging of disease severity relies on rASRM and #ENZIAN classifications. Evidence from comparative and cohort studies suggests that both laparoscopic and robotic MIS can improve QoL and reproductive outcomes; however, the magnitude of benefit varies across studies, patient phenotypes, and follow-up periods. Conclusions: MIS is an increasingly used therapeutic option for DIE, with growing evidence of improvement in pain and QoL, but current data remain heterogeneous and do not uniformly support superiority over other approaches. Routine incorporation of validated QoL instruments and fertility indices into both clinical practice and research is essential to better stratify patients, support shared decision-making, and optimize long-term outcomes. Full article

Background/Objectives: The aging population faces a multitude of health challenges, particularly when it comes to maintaining proper nutrition. Age-related physiological changes, such as decreased metabolism, diminished taste perception, and difficulty in chewing, can lead to insufficient nutrient intake, ultimately resulting in malnutrition. It is crucial to address these issues to promote not only physical health but also overall well-being. In this modern era, artificial intelligence (AI) technologies, including robots and machine learning algorithms, are being increasingly harnessed to encourage healthy eating habits among older adults. This is critical to support healthy aging and mitigate diet-related chronic diseases. However, little or no synthesis has established their effectiveness in delivering personalized, scalable, and adaptive interventions for older adults. This systematic review considers the state-of-the-art application of AI-based interventions aimed at improving dietary behaviors and nutritional outcomes in older adults. Methods: Following the PRISMA 2020 guidelines and a registered PROSPERO protocol (ID: CRD420241045268), we systematically analyzed 30 studies we collected from five databases, published between 2015 and 2025 based on different AI techniques, including machine learning, natural language processing, and recommender systems. We synthesized data collected from these studies to examine the intervention types, outcomes, and methodological approaches. Results: Findings from our review highlight the potential of AI-based interventions to promote engagement among older adults and improve adherence to healthy eating guidelines. Additionally, we found some challenges related to ethical concerns such as privacy and transparency, and limited evidence of their long-term effectiveness. Conclusions: AI-based interventions offer significant promise in promoting healthy eating among older adults through personalized, adaptive, and scalable interventions. Yet, current evidence is constrained by some methodological limitations and ethical concerns, which calls for future research to design inclusive, evidence-based AI interventions that address the unique physiological, psychological, and social needs of older adults. Full article

Integrating human–robot collaboration (HRC) into construction sites has significantly enhanced efficiency and quality. However, it also introduces new or intensifies existing risks as it brings in new entities, relationships, and construction activities. Safety remains the top priority and a persistent concern in HRC systems. However, the current literature on human–robot collaboration safety (HRCS) is vast yet fragmented, and a systematic exploration of its status and research trends in the construction context is still lacking. This paper explores advances in HRCS over the past two decades through a mixed quantitative and qualitative analysis method. Initially, 287 related articles were identified by keyword-searching in Scopus, followed by bibliometric analysis using CiteSpace to uncover the knowledge structure and track emerging research trends. Subsequently, a qualitative discussion highlights achievements in HRCS across five dimensions: (1) optimization of remote intelligent machinery; (2) hazard analysis and risk assessment in HRCS; (3) digital twin for safety monitoring; (4) cognitive and psychological impacts; (5) organizational management perspective. This study quantitatively maps the scientific landscape of HRCS at a macro level and qualitatively identifies key research areas. It provides a comprehensive foundation for understanding the evolution of HRCS and exploring future research directions and applications. Full article

With the rapid advancement of artificial intelligence, the deployment of social robots has significantly broadened, extending into diverse fields such as education, medical services, and business. Despite this expansive growth, there remains a notable scarcity of empirical research addressing the underlying psychological mechanisms that influence human–robot interactions. To address this critical research gap, the present study proposes and empirically tests a theoretical model designed to elucidate how users’ multi-dimensional perceived values of social robots influence their attitudinal responses and outcomes. Based on questionnaire data from 569 social robot users, the study reveals that users’ perceived utilitarian value, emotional value, and hedonic value all exert significant positive effects on active involvement, thereby fostering their identification and reinforcing attitudinal loyalty. Among these dimensions, emotional value emerged as the strongest predictor, underscoring the pivotal role of emotional orientation in cultivating lasting human–robot relationships. Furthermore, the findings highlight the critical mediating function of active involvement in linking perceived value to users’ psychological sense of belonging, thereby elucidating the mechanism through which perceived value enhances engagement and promotes sustained long-term interaction. These findings extend the conceptual boundaries of human–machine interaction, offer a theoretical foundation for future explorations of user psychological mechanisms, and inform strategic design approaches centered on emotional interaction and user-oriented experiences, providing practical guidance for optimizing social robot design in applications. Full article

The integration of AI-powered plush robots in educational and therapeutic settings for children with Autism Spectrum Disorders (ASD) necessitates a robust interdisciplinary methodology to evaluate usability, psychological impact, and therapeutic efficacy. This study proposes and applies a four-phase research framework designed to guide the development and assessment of AI-powered plush robots for social rehabilitation and education. Phase 1 involved semi-structured interviews with 13 ASD specialists to explore robot applications. Phase 2 tested initial usability with typically developing children (N = 10–15) through structured sessions. Phase 3 involved structured interaction sessions with children diagnosed with ASD (N = 6–8) to observe the robot’s potential for rehabilitation, observed by specialists and recorded on video. Finally, Phase 4 synthesized data via multidisciplinary triangulation. Results highlighted the importance of iterative, stakeholder-informed design, with experts emphasizing visual properties (color, texture), psychosocial aspects, and adjustable functions. The study identified key technical and psychological evaluation criteria, including engagement, emotional safety, and developmental alignment with ASD intervention models. Findings underscore the value of qualitative methodologies and phased testing in developing child-centered robotic tools. The research establishes a robust methodological framework and provides preliminary evidence for the potential of AI-powered plush robots to support personalized, ethically grounded interventions for children with ASD, though their therapeutic efficacy requires further longitudinal validation. This methodology bridges engineering innovation with psychological rigor, offering a template for future assistive technology research by prioritizing a rigorous, stakeholder-centered design process. Full article

The incorporation of educational robotics (ER) into classroom learning has emerged as a significant goal in contemporary education, with instructors assuming a pivotal role. Recent research has shown the influence of teachers’ perceptions of ER and their self-efficacy on the learning process, while the primary goal in these inquiries is to the development of appropriate scales that guarantee correct measurements. Serving this goal, the present study presents the TRi-Robotics scale and its psychometric properties, which assesses teachers’ readiness to integrate ER into their classrooms. TRi-Robotics is a novel multidimensional tool that integrates self-efficacy, commitment, and affective conditions, validated through both CFA and network psychometrics. The proposed 14-item scale is three-dimensional and includes self-efficacy (SE), commitment (C), and affective conditions (AC). The validation procedure included the customary Exploratory and Confirmatory Factor Analysis, applied to a sample of 817 in-service teachers. Reliability analysis showed satisfactory internal consistency, while measurement invariance for gender was sustained. Furthermore, network psychometrics was applied via Exploratory Graph Analysis (EGA), which supported the proposed structure and its dimensionality and measurement invariance. The TRi-Robotics scale proved a valid instrument with satisfactory psychometric properties, and it is a significant asset to implement in educational and psychological research for testing further research hypotheses. Full article

Increasing automation and the rapidly growing use of robots in industrial as well as social areas result in a greater need for research regarding collaboration between humans and robots. Key factors for a safe and successful combination of human and robot abilities include acceptance and trust in the robot. In order to prevent physical and psychological harm to humans, reducing these negative emotions and increasing trust and acceptance are essential. One way to achieve this is through the use of virtual training methods and environments. However, current research scarcely covers this approach. Therefore, this research focusses on an experimental approach to investigate emotional and psychophysiological (ECG, EDA) reactions while performing a collaborative assembly task (screwing) with an industrial robot in a real and a virtual setting, respectively. The study sample consisted of 46 participants (23 female) with an age range from 20 to 58 years. The results of the analyzed subjective and objective psychophysiological (cardiovascular and electrodermal responses) measures provide more information regarding the suitability of virtual trainings for human–robot collaboration. Differences in task complexity were measurable in both virtual and real environments. Furthermore, gender differences were also shown. Full article

Personality is a fundamental psychological trait that exerts a long-term influence on human behavior patterns and social interactions. Automatic personality recognition (APR) has exhibited increasing importance across various domains, including Human–Robot Interaction (HRI), personalized services, and psychological assessments. In this study, we propose a multimodal personality recognition model that classifies the Big Five personality traits by extracting features from three heterogeneous sources: audio processed using Wav2Vec2, video represented as Skeleton Landmark time series, and text encoded through Bidirectional Encoder Representations from Transformers (BERT) and Doc2Vec embeddings. Each modality is handled through an independent Self-Attention block that highlights salient temporal information, and these representations are then summarized and integrated using a late fusion approach to effectively reflect both the inter-modal complementarity and cross-modal interactions. Compared to traditional recurrent neural network (RNN)-based multimodal models and unimodal classifiers, the proposed model achieves an improvement of up to 12 percent in the F1-score. It also maintains a high prediction accuracy and robustness under limited input conditions. Furthermore, a visualization based on t-distributed Stochastic Neighbor Embedding (t-SNE) demonstrates clear distributional separation across the personality classes, enhancing the interpretability of the model and providing insights into the structural characteristics of its latent representations. To support real-time deployment, a lightweight thread-based processing architecture is implemented, ensuring computational efficiency. By leveraging deep learning-based feature extraction and the Self-Attention mechanism, we present a novel personality recognition framework that balances performance with interpretability. The proposed approach establishes a strong foundation for practical applications in HRI, counseling, education, and other interactive systems that require personalized adaptation. Full article

In the context of rapidly evolving industrial ecosystems, the human–machine interaction (HMI) has shifted from basic interface control toward complex, adaptive, and human-centered systems. This review explores the multidisciplinary foundations and technological advancements driving this transformation within Industry 4.0 and the emerging paradigm of Industry 5.0. Through a comprehensive synthesis of the recent literature, we examine the cognitive, physiological, psychological, and organizational factors that shape operator performance, safety, and satisfaction. A particular emphasis is placed on ergonomic interface design, real-time physiological sensing (e.g., EEG, EMG, and eye-tracking), and the integration of collaborative robots, exoskeletons, and extended reality (XR) systems. We further analyze methodological frameworks such as RULA, OWAS, and Human Reliability Analysis (HRA), highlighting their digital extensions and applicability in industrial contexts. This review also discusses challenges related to cognitive overload, trust in automation, and the ethical implications of adaptive systems. Our findings suggest that an effective HMI must go beyond usability and embrace a human-centric philosophy that aligns technological innovation with sustainability, personalization, and resilience. This study provides a roadmap for researchers, designers, and practitioners seeking to enhance interaction quality in smart manufacturing through cognitive ergonomics and intelligent system integration. Full article

Background: Neurologically impaired children often face severe gastroesophageal reflux disease (GERD), feeding difficulties, and related challenges, profoundly impacting their quality of life (QoL) and that of their caregivers. Surgery is often necessary to alleviate symptoms in this population, and the success of surgical treatment, along with the achievement of clinical endpoints, must also consider the impact on QoL. The aim of this study is to evaluate the impact of fundoplication surgery on the QoL of both children and caregivers. Methods: All patients treated between 2010 and 2023 at the Pediatric Surgery Department of San Matteo Hospital in Pavia were included in the study. The modified 1996 O’Neill questionnaire was identified as a suitable model for a QoL survey. QoL assessments included caregiver-reported outcomes using validated questionnaires, focusing on physical, psychological, and social domains. Patients with a follow-up period of less than 12 months were excluded. As a secondary outcome, we evaluated the satisfaction of patients treated after 2020 who received integrated care through a multidisciplinary outpatient clinic. Results: Among the 77 patients, 42 were treated between 2010 and 2021. Of these, 16 participated in pre- and post-operative QoL evaluations, showing significant improvements in GERD resolution, feeding ease, and caregiver stress. From 2020, 35 patients benefited from a multidisciplinary approach; 12 underwent robotic fundoplication. Feeding ease scores improved significantly (mean increase from 37.5 to 84.2; p < 0.001), while caregiver stress scores decreased by 35% (p < 0.01). Conclusions: The combination of surgical and multidisciplinary interventions significantly enhances QoL for SNI children and their families. Integrated care models provide a framework for addressing complex needs and should be prioritized in clinical practice. Full article

While deep neural networks demonstrate robust performance in visual tasks, the long-tail distribution of real-world data leads to significant recognition accuracy degradation in critical scenarios such as medical human–robot affective interaction, particularly the misidentification of low-frequency negative emotions (e.g., fear and disgust) that may trigger psychological resistance in patients. Here, we propose a method based on dynamic intra-class clustering (DICC) to optimize the class imbalance problem in facial expression recognition tasks. The DICC method dynamically adjusts the distribution of majority classes by clustering them into subclasses and generating pseudo-labels, which helps the model learn more discriminative features and improve classification accuracy. By comparing with existing methods, we demonstrate that the DICC method can help the model achieve superior performance across various facial expression datasets. In this study, we conducted an in-depth evaluation of the DICC method against baseline methods using the FER2013, MMAFEDB, and Emotion-Domestic datasets, achieving improvements in classification accuracy of 1.73%, 1.97%, and 5.48%, respectively. This indicates that the DICC method can effectively enhance classification precision, especially in the recognition of minority class samples. This approach provides a novel perspective for addressing the class imbalance challenge in facial expression recognition and offers a reference for future research and applications in related fields. Full article

This narrative review explores the significant evolution of sports rehabilitation, tracing its trajectory from basic exercise therapies of the early 20th century to the advanced, neuroplasticity-driven approaches of the 21st century, with a specific focus on anterior cruciate ligament reconstruction (ACLR). The primary aim is to understand how neuroplasticity, motor control, and sensorimotor retraining can optimize recovery, reduce reinjury risk, and enhance long-term athletic performance, and to synthesize current rehabilitation strategies that integrate innovative technologies, such as robotics, virtual reality (VR), and biofeedback systems, to address the neurocognitive deficits that contribute to the alarmingly high reinjury rates (9–29%) observed in young athletes post-ACLR. These deficits include impaired proprioception, motor control, and psychological factors like fear of reinjury. The methodology employed involves a narrative review of peer-reviewed literature from databases including PubMed, Scopus, and Web of Science. The synthesis of findings underscores the importance of holistic rehabilitation approaches, including targeted proprioceptive exercises, dual-task drills, and immersive VR training, in enhancing sensorimotor integration, decision-making, and athlete confidence. Furthermore, this review highlights the critical need for long-term monitoring and interdisciplinary collaboration between neuroscientists, physiotherapists, and engineers to refine rehabilitation protocols and ensure sustained recovery. By leveraging neuroplasticity and advanced technologies, the field can shift from a focus on purely physical restoration to comprehensive recovery models that significantly reduce reinjury risks and optimize athletic performance. Full article

Objectives: This study aims to develop performance indicators for service robotics in senior community-based environments and analyze their impact on independent living and quality of life for older adults. Methods: To achieve this, a sequential exploratory design within the Mixed Methods Research (MMR) framework was employed, integrating qualitative research (Focus Group Interview, FGI) and quantitative research (Analytic Hierarchy Process, AHP). The FGIs were conducted with a panel of six experts over three rounds, leading to the identification of six key performance indicators (KPIs) for service robotics in senior communities: Technical Performance, User-Centered Performance, Social and Psychological Impact, Ethical and Safety Performance, Economic and Operational Performance, and Service Efficiency. Following this, the AHP analysis was conducted with a final sample of 29 participants from an initial 32 respondents. Results: The AHP analysis results revealed that Technical Performance (rank 1, 0.256) was the most critical factor, followed by User-Centered Performance (rank 2, 0.205) and Social and Psychological Impact (rank 3, 0.167). These findings suggest that enhancing a user-friendly, intuitive UI/UX is essential for ensuring ease of use by older adults. Additionally, while Ethical and Safety Performance (rank 3, 0.139), Economic and Operational Performance (rank 4, 0.126), and Service Efficiency (rank 5, 0.105) had relatively lower importance scores, the study highlights the necessity of establishing optimized systems through ethical and safety standards and emphasizes that real-time monitoring systems play a crucial role in enhancing operational efficiency. Conclusions: Enhancing service robotics performance requires prioritizing technical capabilities and user-centered design, along with ethical standards and real-time monitoring. This study proposes a structured evaluation framework to support more effective robotic solutions in senior care environments. Full article