Search Results (412)

Wheelchair-mounted assistive robotic manipulators can provide reach and grasp functions for power wheelchair users. This in-lab study evaluated a vision-guided shared control (VGS) system with twelve users completing two multi-step kitchen tasks: a drinking task and a popcorn making task. Using a mixed methods approach participants compared VGS and manual joystick control, providing performance metrics, qualitative insights, and lessons learned. Data collection included demographic questionnaires, the System Usability Scale (SUS), NASA Task Load Index (NASA-TLX), and exit interviews. No significant SUS differences were found between control modes, but NASA-TLX scores revealed VGS control significantly reduced workload during the drinking task and the popcorn task. VGS control reduced operation time and improved task success but was not universally preferred. Six participants preferred VGS, five preferred manual, and one had no preference. In addition, participants expressed interest in robotic arms for daily tasks and described two main operation challenges: distinguishing wrist orientation from rotation modes and managing depth perception. They also shared perspectives on how a personal robotic arm could complement caregiver support in their home. Full article

The rapid increase in the global population of older adults presents a significant challenge, but also a unique opportunity to leverage technological advancements for promoting independent living and well-being. This study introduces the CIREI framework, which is a comprehensive model designed to enhance the integration of smart home and assistive technologies specifically for pre-frail older adults. Developed through a systematic literature review and innovative and comprehensive co-design activities, the CIREI framework captures the nuanced needs, preferences, and challenges faced by older adults, caregivers, and experts. Key findings from the co-design workshop highlight critical factors such as usability, privacy, and personalised learning preferences, which directly influence technology adoption. These insights informed the creation of an intelligent middleware prototype named WISE-WARE, which seamlessly integrates commercial off-the-shelf (COTS) devices to support health management and improve the quality of life for older adults. The CIREI framework’s adaptability ensures it can be extended and refined to meet the ever-changing needs of the ageing population, providing a robust foundation for future research and development in user-centred technology design. All workshop materials, including tools and methodologies, are made available to encourage the further exploration and adaptation of the CIREI framework, ensuring its relevance and effectiveness in the dynamic landscape of ageing and technology. This research contributes significantly to the discourse on ageing in place, digital inclusion, and the role of technology in empowering older adults to maintain independence. Full article

In the context of global demographic aging, falls among the elderly remain a major public health concern, often leading to injury, hospitalization, and loss of autonomy. This study proposes a real-time fall detection system that combines a modern computer vision model, YOLOv11 with integrated pose estimation, and an Artificial Intelligence (AI)-based voice assistant designed to reduce false alarms and improve intervention efficiency and reliability. The system continuously monitors human posture via video input, detects fall events based on body dynamics and keypoint analysis, and initiates a voice-based interaction to assess the user’s condition. Depending on the user’s verbal response or the absence thereof, the system determines whether to trigger an emergency alert to caregivers or family members. All processing, including speech recognition and response generation, is performed locally to preserve user privacy and ensure low-latency performance. The approach is designed to support independent living for older adults. Evaluation of 200 simulated video sequences acquired by the development team demonstrated high precision and recall, along with a decrease in false positives when incorporating voice-based confirmation. In addition, the system was also evaluated on an external dataset to assess its robustness. Our results highlight the system’s reliability and scalability for real-world in-home elderly monitoring applications. Full article

The integration of digital serious games into speech learning (rehabilitation) has demonstrated significant potential in enhancing accessibility and inclusivity for children with speech disabilities. This review of the state of the art examines the role of serious games, Artificial Intelligence (AI), and Natural Language Processing (NLP) in speech rehabilitation, with a particular focus on interaction modalities, engagement autonomy, and motivation. We have reviewed 45 selected studies. Our key findings show how intelligent tutoring systems, adaptive voice-based interfaces, and gamified speech interventions can empower children to engage in self-directed speech learning, reducing dependence on therapists and caregivers. The diversity of interaction modalities, including speech recognition, phoneme-based exercises, and multimodal feedback, demonstrates how AI and Assistive Technology (AT) can personalise learning experiences to accommodate diverse needs. Furthermore, the incorporation of gamification strategies, such as reward systems and adaptive difficulty levels, has been shown to enhance children’s motivation and long-term participation in speech rehabilitation. The gaps identified show that despite advancements, challenges remain in achieving universal accessibility, particularly regarding speech recognition accuracy, multilingual support, and accessibility for users with multiple disabilities. This review advocates for interdisciplinary collaboration across educational technology, special education, cognitive science, and human–computer interaction (HCI). Our work contributes to the ongoing discourse on lifelong inclusive education, reinforcing the potential of AI-driven serious games as transformative tools for bridging learning gaps and promoting speech rehabilitation beyond clinical environments. Full article

Background: Primary progressive aphasia (PPA) is a neurodegenerative disease characterised by progressive impairment of speech and language abilities. Intensive speech and language teletherapy combined with remotely supervised, self-administered transcranial direct current stimulation (tDCS) may be suited to remove barriers to accessing potentially effective treatments, but there is only limited evidence on the feasibility of this combined approach. Methods: This pilot case series investigated the feasibility, tolerability and preliminary efficacy of a novel telerehabilitation programme combined with home-based, self-administered tDCS for people with primary progressive aphasia (pwPPA). The intervention programme was co-developed with pwPPA and their caregivers, to reflect their priorities regarding treatment content and outcomes (i.e., naming, functional communication). Results: Two pwPPA successfully completed the telerehabilitation intervention with daily naming training and communicative-pragmatic therapy paired with tDCS, over 10 consecutive workdays. Caregivers assisted in the setup of equipment required for teletherapy and home-based tDCS. Participants successfully completed the programme with a 95% completion rate. Home-based tDCS was well tolerated. Both participants showed improvements in naming and communication, suggesting preliminary efficacy of the intervention. Conclusions: Overall, this study demonstrates the feasibility and potential benefit of a novel, easily accessible and patient-relevant telerehabilitation intervention for pwPPA, which requires confirmation in a future larger-scale exploratory trial. Full article

This study aimed to investigate food insecurity (FI) and its association with sociodemographic characteristics and behavioral and dental alterations in adolescents from a county in the inland of northeastern Brazil. Data on 192 adolescents aged 11–14 years were analyzed in the public school system in Juripiranga, Paraíba, Northeast Brazil. The adolescents and their guardians responded to the Brazilian Food Insecurity Scale, the Strength and Difficulties Questionnaire for the caregiver, the Strength and Difficulties Questionnaire for children and adolescents, and a sociodemographic questionnaire. Academic performance was observed by calculating the median of the final assessments of basic school subjects. The prevalence of FI was 69.19, and FI was associated with a family income of up to one minimum wage per month (prevalence ratio [PR]: 1.90; 95% confidence interval [95% CI]: 1.20–3.01), no practice of religion by the guardian (PR: 1.34; 95% CI: 1.04–1.73), behavior considered inappropriate by the guardian (PR: 1.33; 95% CI: 1.02–1.73), and academic performance (PR: 1.35; 95% CI: 1.05–1.72). FI is considered a complex and multifactorial problem that requires appropriate intervention to deal with multiple social determinants. The results point to the need for integrated public policies between the health, education, social assistance, and food security sectors. Full article

The transition from hospital-based palliative care to home care is a critical phase often marked by logistical, medical, and emotional challenges. Effective discharge planning is essential to ensure continuity of care, yet gaps in communication, interdisciplinary coordination, and access to resources frequently hinder this process. This qualitative study explored key barriers, related support needs, and strategies for optimising palliative care discharge through semi-structured interviews with 28 participants, including healthcare professionals, recently discharged palliative care patients, and primary caregivers. Reflexive thematic analysis revealed five main themes: (1) discharge planning and coordination; (2) symptom management and medication; (3) psychosocial support; (4) communication and information; (5) the role of assistive devices and home care services. Discharge processes were often late or unstructured. Poor interdisciplinary collaboration and a lack of caregiver preparation also contributed to hospital readmissions and emotional distress. By focusing on needs, our analysis identifies not only what was lacking but also what is required to overcome these barriers. Our findings suggest that standardised discharge protocols and checklists, earlier planning, structured communication tools, and improved integration of home care services could enhance patient outcomes and reduce caregiver burden. Addressing psychosocial needs and ensuring timely access to assistive devices are also crucial. Strengthening interdisciplinary collaboration and refining discharge practices can facilitate smoother transitions and improve the quality of palliative care at home. Full article

Background/Objectives: This study examines the subjective perceptions of older adults with mild chronic conditions regarding an artificial intelligence (AI)-based caregiving device, referred to as an “AI Human”, by identifying and categorising their viewpoints through Q methodology. Methods: We conducted the study in February 2025 at two adult welfare centres in Buyeo, South Korea. Thirteen older adults used the AI Human device with support for at least 15 days. We initially generated 152 opinion statements through a literature review, focus group interviews, and AI-assisted methods and refined them to a Q sample of 34 statements. Participants completed Q sorts, and we used Ken-Q Analysis software (version 2.0.1) to analyse the data, applying principal component analysis and Varimax rotation. Results: Four distinct perception types emerged: (1) emotionally engaged users prioritise reminiscence and emotional interaction; (2) present-oriented conversationalists prefer real-time, everyday dialogue; (3) usage-burdened users are interested in the device but experience usage difficulty; and (4) function-oriented users value health and caregiving functions. Conclusions: The acceptance of AI caregiving devices among older adults varies based on their emotional needs, conversation preferences, technical accessibility, and perceived usefulness. This study provides theoretical and practical insights for developing personalised, elder-friendly AI systems that support ageing and promote emotional well-being. Full article

Social robots in Ambient Assisted Living (AAL) environments offer a promising alternative for enhancing senior care by providing companionship and functional support. These robots can serve as intuitive interfaces to complex smart home systems, allowing seniors and caregivers to easily control their environment and access various assistance services through natural interactions. By combining the emotional engagement capabilities of social robots with the comprehensive monitoring and support features of AAL, this integrated approach can potentially improve the quality of life and independence of elderly individuals while alleviating the burden on human caregivers. This paper explores the integration of social robotics with ambient assisted living (AAL) technologies to enhance elderly care. We propose a novel framework where a social robot is the central orchestrator of an AAL environment, coordinating various smart devices and systems to provide comprehensive support for seniors. Our approach leverages the social robot’s ability to engage in natural interactions while managing the complex network of environmental and wearable sensors and actuators. In this paper, we focus on the technical aspects of our framework. A computational P2P notebook is used to customize the environment and run reactive services. Machine learning models can be included for real-time recognition of gestures, poses, and moods to support non-verbal communication. We describe scenarios to illustrate the utility and functionality of the framework and how the robot is used to orchestrate the AAL environment to contribute to the well-being and independence of elderly individuals. We also address the technical challenges and future directions for this integrated approach to elderly care. Full article

The rapid growth of China’s aging population has made elderly care a pressing social issue. Due to an imperfect pension system, limited uptake of institutional care, and uneven regional economic development, most elderly people in China still rely on home-based care. Elderly people living at home are usually cared for by their family, partners, caregivers, or themselves. However, this often fails to meet their complex health, safety, and emotional needs. Artificial intelligence may provide promising solutions to improve home care experiences and address the multifaceted health and lifestyle challenges faced by homebound elderly people. This review explores the applications of artificial intelligence in home-based care from four main perspectives: home health care, home safety and security, smart life assistants, and psychological care and emotional support. We systematically searched PubMed, IEEE Xplore, CNKI, and Scopus databases, integrated the latest research published between 2015 and 2024, focused on peer-reviewed, practice-oriented research, and reviewed relevant technology development paths and the current status of the field. Unlike previous studies that focused on physiological monitoring, this study is the first to systematically and comprehensively evaluate the role of artificial intelligence in improving the convenience of daily life and mental health support for elderly people at home. By comprehensively reviewing and analyzing the basic principles and application background of artificial intelligence technology in this field, we summarize the current technical and ethical challenges and propose future research directions. This study aims to help readers gain a deeper understanding of the current status and emerging trends of artificial intelligence-enabled home-based elderly care, thereby providing valuable insights for continued innovation and application in this rapidly developing field. Full article

Parents provide most of the support needed for children with cerebral palsy (CP) to increase the child’s participation and independence. Understanding the experiences of parents caring for children with CP is essential for developing effective family programs and services. The current knowledge about parents’ experiences in CP is based on studies in Western countries, with little known about this phenomenon in Arab countries like Saudi Arabia. This study aimed to understand the unique experiences and support needs of Saudi parents caring for children with CP from a social-ecological perspective. We conducted a qualitative, exploratory, descriptive study involving 12 semi-structured interviews with mothers and fathers of children with different types of CP. We analyzed the data using a reflexive thematic approach, following six distinct phases. Participants’ narratives revealed a complex caregiving journey marked by both challenges and rewards. Support from Saudi nuclear and extended family members was considered important; however, many parents expressed a need for additional physical and financial assistance from their families. Parents reported feeling stressed and experiencing challenges in accessing and navigating educational and healthcare services. Our findings highlight that Islamic values play a crucial role in the experiences of Saudi parents. These values foster a sense of collectivism, highlighting the importance of family support and community involvement, which can affect the Saudi caregiving environment. Parents remain an essential yet often invisible part of the Saudi caregiving system. Without adequate support, parents are at risk of experiencing social, financial, academic, physical, and mental health challenges, which may affect their overall family well-being. Future work may need to consider spiritual and gender roles when developing programs or services to support Saudi parents of children with CP. Full article

This paper describes the modelling of Key Health Indicators (KHI) of frail individuals through non-invasive sensors located in their environment and wearable devices. Primary care professionals defined four indicators for daily health monitoring: sleep patterns, excretion control, physical mobility, and caregiver social interaction. A minimally invasive and low-cost sensing architecture was implemented, combining indoor localisation and physical activity tracking through environmental sensors and wrist-worn wearables. The health outcomes are modelled using a knowledge-based framework that integrates knowledge graphs to represent control variables and their relationships with data streams, and fuzzy logic to linguistically define temporal patterns based on expert criteria. The proposed approach was validated in a real-world case study with an older adult living independently in Granada, Spain. Over several days of deployment, the system successfully generated interpretable daily summaries reflecting relevant behavioural patterns, including rest periods, bathroom usage, activity levels, and caregiver proximity. In addition, supervised machine learning models were trained on the indicators derived from the fuzzy logic system, achieving average accuracy and F1 scores of 93% and 92%, respectively. These results confirm the potential of combining expert-informed semantics with data-driven inference to support continuous, explainable health monitoring in ambient assisted living environments. Full article

The development of Facial Expression Recognition (FER) technology has significantly enhanced the naturalness and intuitiveness of human-robot interaction. In the field of service robots, particularly in applications such as production assistance, caregiving, and daily service communication, efficient FER capabilities are crucial. However, existing Convolutional Neural Network (CNN) models still have limitations in terms of feature representation and recognition accuracy for facial expressions. To address these challenges, we propose CAGNet, a novel network that combines multiscale feature aggregation and attention mechanisms. CAGNet employs a deep learning-based hierarchical convolutional architecture, enhancing the extraction of features at multiple scales through stacked convolutional layers. The network integrates the Convolutional Block Attention Module (CBAM) and Global Average Pooling (GAP) modules to optimize the capture of both local and global features. Additionally, Batch Normalization (BN) layers and Dropout techniques are incorporated to improve model stability and generalization. CAGNet was evaluated on two standard datasets, FER2013 and CK+, and the experiment results demonstrate that the network achieves accuracies of 71.52% and 97.97%, respectively, in FER. These results not only validate the effectiveness and superiority of our approach but also provide a new technical solution for FER. Furthermore, CAGNet offers robust support for the intelligent upgrade of service robots. Full article

Background: The ageing of the population and the progressive increase in chronic diseases represent a major challenge for healthcare systems. The community nurse case manager (CNCM) is emerging as a key figure to provide comprehensive and continued care for complex and pluripathological chronic patients (CPCPs), especially after hospital discharge. Objective: The aim of this study is to pilot CNCMs in assisting CPCPs and assess their effects on functional capacity, cognitive performance, quality of life, readmissions, clinical parameters, satisfaction with home care, and caregiver overload. Methods: A comparative study will be carried out at two health centres in Salamanca (Spain). In both centres, CPCPs will continue to receive the interventions included in the Castilla y León Health System Portfolio from their primary care (PC) nurses. In the intervention centre, case management provided by a CNCM will be added. We will recruit 212 CPCPs with cardiac or respiratory disease and/or diabetes mellitus who are dependent for basic activities of daily living and have a programmed hospital discharge. An initial assessment will be performed at home after discharge, followed by assessments at 3, 6, and 12 months. Expected results: The intervention is anticipated to improve all study outcomes. Discussion: CNCMs may contribute to more proactive and individualised follow-up care for CPCPs and their caregivers, improving care coordination. Conclusions: This study will help to evaluate the feasibility and clinical relevance of incorporating the CNCM’s role into PC. This study was registered at ClinicalTrials.gov with the identifier NCT06155591. The date of trial registration was 24 November 2023. Full article

Background/Objectives: This study aimed to assess the reliability and validity of the Korean version of the Eating and Drinking Ability Classification System (EDACS) for children with cerebral palsy (CP). Methods: This was a prospective cross-sectional study conducted to psychometrically assess 40 children with CP. Reliability was evaluated by a physician in pediatric rehabilitation, a speech–language therapist (SaLT), and caregivers. Results: In the evaluation of each level and level of assistance in the EDACS, the agreement observed between the physician and speech therapist was almost perfect (κW = 0.940, 0.919). The agreement between the physician and the caregiver was substantial (κW = 0.618, 0.592), whereas it was moderate between the therapist and caregiver (κW = 0.557, 0.556). Intra-rater reliability remained almost perfect, with the physician (κW = 0.979), the SaLT (κW = 0.980), and caregivers (κW = 0.980). The EDACS showed moderate to high correlation with the Functional Oral Intake Scale, Gross Motor Function Classification System, Manual Ability Classification System, and Communication Function Classification System (Kτ = −0.863, 0.656, 0.720, 0.616). Conclusions: These findings support the Korean EDACS as a reliable tool for classifying eating and drinking abilities in children with CP, thereby enhancing its clinical utility and communication among non-professionals and professionals. Full article