Search Results (927)

Background/Objective: Understanding the training effect in high-level running is important for performance optimization and injury prevention. This includes awareness of how different running surface types (e.g., hard versus soft) may modify biomechanics. Recent studies have demonstrated that deep learning algorithms, such as convolutional neural networks (CNNs), can accurately classify human activity collected via body-worn sensors. To date, no study has assessed optimal signal type, sensor location, and model architecture to classify running surfaces. This study aimed to determine which combination of signal type, sensor location, and CNN architecture would yield the highest accuracy in classifying grass and asphalt surfaces using inertial measurement unit (IMU) sensors. Methods: Running data were collected from forty participants (27.4 years + 7.8 SD, 10.5 ± 7.3 SD years of running) with a full-body IMU system (head, sternum, pelvis, upper legs, lower legs, feet, and arms) on grass and asphalt outdoor surfaces. Performance (accuracy) for signal type (acceleration and angular velocity), sensor configuration (full body, lower body, pelvis, and feet), and CNN model architecture was tested for this specific task. Moreover, the effect of preprocessing steps (separating into running cycles and amplitude normalization) and two different data splitting protocols (leave-n-subject-out and subject-dependent split) was evaluated. Results: In general, acceleration signals improved classification results compared to angular velocity (3.8%). Moreover, the foot sensor configuration had the best performance-to-number of sensor ratio (95.5% accuracy). Finally, separating trials into gait cycles and not normalizing the raw signals improved accuracy by approximately 28%. Conclusion: This analysis sheds light on the important parameters to consider when developing machine learning classifiers in the human activity recognition field. A surface classification tool could provide useful quantitative feedback to athletes and coaches in terms of running technique effort on varied terrain surfaces, improve training personalization, prevent injuries, and improve performance. Full article

This paper contains an analysis of methods for person classification based on signals from wearable IMU sensors during sports. While this problem has been investigated in prior work, existing approaches have not addressed it within the context of few-shot or minimal-data scenarios. A few-shot scenario is especially useful as the main use case for person identification in sports systems is to be integrated into personalised biofeedback systems in sports. Such systems should provide personalised feedback that helps athletes learn faster. When introducing a new user, it is impractical to expect them to first collect many recordings. We demonstrate that the problem can be solved with over 90% accuracy in both open-set and closed-set scenarios using established methods. However, the challenge arises when applying few-shot methods, which do not require retraining the model to recognise new people. Most few-shot methods perform poorly due to feature extractors that learn dataset-specific representations, limiting their generalizability. To overcome this, we propose a combination of an unsupervised feature extractor and a prototypical network. This approach achieves 91.8% accuracy in the five-shot closed-set setting and 81.5% accuracy in the open-set setting, with a 99.6% rejection rate for unknown athletes. Full article

Background: Mild cognitive impairment (MCI) affects up to 40% of patients with Parkinson’s disease (PD), yet conventional rehabilitation often lacks engagement. The OPERA project developed a novel Bio-cooperative Robotic Platform (PRoBio), integrating a service robot and a virtual reality-based rehabilitation for personalized cognitive training. This work presents two preliminary user-centred studies aimed to assess PRoBio usability and acceptability. Methods: to gather qualitative feedback on robotic and virtual reality technologies, through ad hoc questionnaires, developed according to participatory design principles and user-centered evaluation literature, Study 1 (Focus group) involved 23 participants: 10 PD patients (F = 6; mean age = 68.9 ± 8.2 years), 5 caregivers (F = 3; mean age = 49.0 ± 15.5), 8 healthcare professionals (F = 6; mean age = 40.0 ± 12.0). Study 2 (Living Lab) tested the final version of PRoBio platform with 6 healthy volunteers (F = 3; mean age = 50.3 ± 11.0) and 8 rehabilitation professionals (F = 3; mean age = 32.8 ± 9.9), assessing usability and acceptability through validated questionnaires. Results: The focus group revealed common priorities across the three groups, including ease of use, emotional engagement, and personalization of exercises. Living Lab unveiled PRoBio as user-friendly, with high usability, hedonic quality, technology acceptance and low workload. No significant differences were found between groups, except for minor concerns on system responsiveness. Discussion: These preliminary findings support the feasibility, usability, and emotional appeal of PRoBio as a cognitive rehabilitation tool. The positive convergence among the groups suggests its potential for clinical integration. Full article

Pain remains one of the most burdensome symptoms in rheumatoid arthritis (RA), often persisting despite inflammatory remission and profoundly impairing quality of life. This review aimed to evaluate the clinical efficacy and mechanistic pathways by which Janus kinase (JAK) inhibitors alleviate RA-related pain. Evidence from randomized clinical trials demonstrates that JAK inhibitors have demonstrated rapid and significant pain relief, often exceeding that of methotrexate or biologic DMARDs. Improvements in patient-reported pain scores seem to typically emerge within 1–2 weeks and are sustained over time. Beyond anti-inflammatory effects, JAK inhibitors modulate central sensitization and nociceptive signaling by attenuating IL-6 and GM-CSF activity, reducing astrocyte and microglial activation, and downregulating nociceptor excitability in dorsal root ganglia and spinal pathways. Preclinical models further suggest that JAK inhibition interrupts neuroimmune feedback loops critical to chronic pain maintenance. Comparative and network meta-analyses consistently position JAK inhibitors among the most effective agents for pain control in RA. However, individual variability in response, partly due to differential JAK-STAT activation and cytokine receptor uncoupling, underscores the need for biomarker-guided treatment approaches. JAK inhibitors represent a mechanistically distinct and clinically impactful class of therapies that target both inflammatory and non-inflammatory pain in RA. Their integration into personalized pain management strategies offers a promising path to address one of RA’s most persistent unmet needs. Full article

People with disabilities often face challenges like moving around independently and depending on personal caregivers for daily life activities. Traditional assistive devices are universally accepted by these communities, but they are designed with one-size-fits-all approaches that cannot adjust to individual human sizes, are not easily customized, and are made from rigid materials that do not adapt as a person’s condition changes over time. This systematic review examines the integration of smart materials, sensors, actuators, and 3D/4D printing technologies in advancing assistive devices, with a particular emphasis on mobility aids. In this work, the authors conducted a comparative analysis of traditional devices with commercially available innovative prototypes and research stage assistive devices by focusing on smart adaptable materials and sustainable additive manufacturing techniques. The results demonstrate how artificial intelligence drives smart assistive devices in hospital decentralized additive manufacturing, and policy frameworks agree with the Sustainable Development Goals, representing the future direction for adaptive assistive technology. Also, by combining 3D/4D printing and AI, it is possible to produce adaptive, affordable, and patient centered rehabilitation with feedback and can also provide predictive and preventive healthcare strategies. The successful commercialization of adaptive assistive devices relies on cost effective manufacturing techniques clinically aligned development supported by cross disciplinary collaboration to ensure scalable, sustainable, and universally accessible smart solutions. Ultimately, it paves the way for smart, sustainable, and clinically viable assistive devices that outperform conventional solutions and promote equitable access for all users. Full article

Emergency medical services confront significant challenges in delivering timely patient assessments within geographically isolated or disaster-impacted regions. While drones (unmanned aircraft systems, UAS) show transformative potential in healthcare, standardized protocols for drone-assisted patient evaluations remain underdeveloped. This study introduces the ABCD-IN-BARS protocol, a 9-step telemedicine checklist integrating patient-assisted maneuvers and drone technology to systematize remote emergency assessments. A wait-list randomized controlled trial with 68 first-aid-trained volunteers evaluated the protocol’s feasibility. Participants underwent web-based modules and in-person simulations and were randomized into immediate training or waitlist control groups. The ABCD-IN-BARS protocol was developed via a content validity approach, incorporating expert-rated items from the telemedicine literature. Outcomes included time-to-assessment, provider confidence (Modified Cooper–Harper Scale), measured at baseline, post-training, and 3-month follow-up. Ethical approval and informed consent were obtained. Most of the participants can complete the assessment with a cue card within 4 min. A mixed-design repeated measures ANOVA assessed the effects of Time (baseline, post-test, 3-month follow-up within subject) on assessment durations. Assessment times improved significantly over three time points (p = 0.008), improving with standardized protocols, while patterns were similar across groups (p = 0.101), reflecting skill retention at 3 months and not affected by injury or not. Protocol adherence in simulated injury identification increased from 63.3% pre-training to 100% post-training. Provider confidence remained high (MCH scores: 2.4–2.7/10), and Technology Acceptance Model (TAM) ratings emphasized strong Perceived Usefulness (PU2: M = 4.48) despite moderate ease-of-use challenges (EU2: M = 4.03). Qualitative feedback highlighted workflow benefits but noted challenges in drone maneuvering. The ABCD-IN-BARS protocol effectively standardizes drone-assisted emergency assessments, demonstrating retained proficiency and high usability. While sensory limitations persist, its modular design and alignment with ABCDE principles offer a scalable solution for prehospital care in underserved regions. Further multicenter validation is needed to generalize findings. Full article

Low-Dimensional Nanohybrids (LDNHs) have emerged as potent multifunctional platforms for neurosensing and neuromodulation, providing elevated spatial-temporal precision, versatility, and biocompatibility. This review examines the intersection of LDNHs with artificial intelligence, brain–computer interfaces (BCIs), and closed-loop neurotechnologies, highlighting their transformative potential in personalized neuro-nano-medicine. Utilizing stimuli-responsive characteristics, optical, thermal, magnetic, and electrochemical LDNHs provide real-time feedback-controlled manipulation of brain circuits. Their pliable and adaptable structures surpass the constraints of inflexible bioelectronics, improving the neuronal interface and reducing tissue damage. We also examined their use in less invasive neurological diagnostics, targeted therapy, and adaptive intervention systems. This review delineates recent breakthroughs, integration methodologies, and fundamental mechanisms, while addressing significant challenges such as long-term biocompatibility, deep-tissue accessibility, and scalable manufacturing. A strategic plan is provided to direct future research toward clinical use. Ultimately, LDNHs signify a transformative advancement in intelligent, tailored, and closed-loop neurotechnologies, integrating materials science, neurology, and artificial intelligence to facilitate the next era of precision medicine. Full article

Modern military operations place significant physiological and cognitive demands on soldiers, necessitating innovative strategies to monitor and optimize health and performance. This narrative review examines the role of continuous physiological state monitoring and precision health strategies to enhance soldier resilience and operational readiness. Advanced wearable biosensors were analyzed for their ability to measure vital physiological parameters—such as heart-rate variability, core temperature, hydration status, and biochemical markers—in real-time operational scenarios. Emerging technological solutions, including AI-driven analytics and edge computing, facilitate rapid data interpretation and predictive health assessments. Results indicate that real-time physiological feedback significantly enhances early detection and prevention of conditions like exertional heat illness and musculoskeletal injuries, reducing medical attrition and improving combat effectiveness. However, ethical challenges related to data privacy, informed consent, and secure data management highlight the necessity for robust governance frameworks and stringent security protocols. Personalized training regimens and rehabilitation programs informed by monitoring data demonstrate potential for substantial performance optimization and sustained force readiness. In conclusion, integrating precision health strategies into military operations offers clear advantages in soldier health and operational effectiveness, contingent upon careful management of ethical considerations and data security. Full article

Language learners increasingly rely on intelligent digital tools to supplement their learning experiences, yet existing chatbots often provide limited support, lacking adaptability, personalization, or domain-specific intelligence. This study introduces a novel AI-powered multi-agent chatbot architecture designed to support English–Arabic translation and language learning. Developed through a three-phase methodology, offline preparation, real-time deployment, and evaluation, the system employs both retrieval-based and generative AI models, with specialized agents managing tasks such as translation, example retrieval, user translation review, and learning feedback. The chatbot was developed using a hybrid architecture incorporating fine-tuned Generative Pre-trained Transformer (GPT) model, sentence embedding techniques, and similarity evaluation metrics. A user study involving 40 undergraduate students and 4 faculty members evaluated the system across usability, effectiveness, and pedagogical value. Results show that the multi-agent chatbot significantly enhanced learner engagement, provided accurate and contextually appropriate language support, and was positively received by both students and instructors. These findings demonstrate the value of multi-agent design in language learning applications and highlight the potential of AI-driven chatbots as intelligent educational assistants. Full article

Creativity is central to innovation and problem-solving, yet scalable training solutions remain limited. This study evaluates Creativeable, an AI-powered creativity training program that provides automated feedback and adjusts creative story writing task difficulty without human intervention. A total of 385 participants completed five rounds of creative story writing using semantically distant word prompts across four conditions: (1) feedback with adaptive difficulty (F/VL); (2) feedback with constant difficulty (F/CL); (3) no feedback with adaptive difficulty (NF/VL); (4) no feedback with constant difficulty (NF/CL). Before and after using Creativeable, participants were assessed for their creativity, via the alternative uses task, as well as undergoing a control semantic fluency task. While creativity improvements were evident across conditions, the degree of effectiveness varied. The F/CL condition led to the most notable gains, followed by the NF/CL and NF/VL conditions, while the F/VL condition exhibited comparatively smaller improvements. These findings highlight the potential of AI to democratize creativity training by offering scalable, personalized interventions, while also emphasizing the importance of balancing structured feedback with increasing task complexity to support sustained creative growth. Full article

Background: The Pharm2Home Initiative’s Community Health Arm adopts a health-equitable approach to chronic disease education and medication therapy management (MTM). We serve senior residents of Solano County, California, who live in affordable housing and have limited financial resources. Aim: This evaluation assesses the uptake of chronic disease management recommendations provided by clinical pharmacists during MTM sessions at community events. Methods: The program engaged clinical pharmacists to provide tailored education and healthcare interventions in senior housing facilities. The goal was to empower seniors to manage their health effectively. The sessions covered various topics, including expired or duplicated medications, incorrect medication use, consultations on medication management, immunizations, and lifestyle adjustments. Results: Over an 18-month period, from January 2022 to August 2023, the program involved 65 participants across ten community health events. These events provided approximately 65 h of direct intervention. Many participants reported significant improvements in understanding their treatment plans and navigating their health needs more confidently. Feedback from 60 seniors after the sessions indicated that 88% felt much better informed about their medications, and 75% expressed that their concerns were addressed extremely well. Conclusions: These outcomes demonstrate the importance of clinical pharmacist-led interventions in improving seniors’ medication use and chronic disease management. The initiative’s approach advocates for integrating clinical pharmacists into community health settings, suggesting a scalable model for enhancing person-centered care. However, further studies are necessary to assess the long-term impacts of these interventions and explore their effectiveness across diverse age groups and more complex conditions. Full article

Universities face growing pressure to deliver personalized learning that prepares students with adaptable, future-ready competencies. Traditional static curricula are often unable to meet these demands. This paper introduces a novel framework based on AI-enhanced digital twins of students (DTS) as dynamic virtual representations integrating academic performance, competency attainment, learning preferences, career objectives, and engagement patterns. The DTS framework employs artificial intelligence algorithms, semantic ontologies spanning educational and career domains, and real-time feedback mechanisms for personalized learning pathway orchestration. To demonstrate the framework’s potential, a simulation study was conducted using synthetic student data. Results compared DTS-guided adaptive pathways with traditional static approaches and showed improvements in competency attainment, engagement, learning efficiency, and reduced dropout risk. Full article

Climate adaptation convenings have emerged in the last decade to share knowledge and accelerate learning in the field. Convenings provide a wealth of information for understanding what issues are being researched and addressed, for evaluating what practices and key components of convenings are considered particularly valuable to practitioners, and for understanding where there are gaps in our knowledge or trends in learning that should be supported. We analyzed survey and attendance data from online and in-person climate convenings in the U.S. to assess perceived outcomes and future intentions, as well as explored thematic changes in sessions at in-person conferences. We performed descriptive analyses on survey and attendance data and conducted thematic analysis of sessions at in-person conferences. Both online and in-person formats achieved high levels of learning and satisfaction reported by respondents, but with higher connectivity and relationship building at in-person events. Topics addressed in forums showed small but meaningful shifts, as some areas of interest increased (e.g., social justice, nature-based solutions) while others decreased (e.g., decision-making tools, infrastructure) or showed variable responses. These trends and feedback provide a foundation for continuing to grow effective practices to support climate adaptation practitioners with the knowledge and opportunities for connection needed to advance the adaptation field. Full article

Distance learning has become essential to higher education, yet its application in military officer training presents unique academic, operational, and security challenges. For Lithuania’s future officers, remote education must foster not only knowledge acquisition but also decision-making, leadership, and operational readiness—competencies traditionally developed in immersive, in-person environments. This study addresses these challenges by integrating System Dynamics Modelling, Contemporary Risk Management Standards (ISO 31000:2022; Dynamic Risk Management Framework), and Learning Analytics to evaluate the interdependencies among twelve critical factors influencing the system resilience and effectiveness of distance military education. Data were collected from fifteen domain experts through structured pairwise influence assessments, applying the fuzzy DEMATEL method to map causal relationships between criteria. Results identified key causal drivers such as Feedback Loop Effectiveness, Scenario Simulation Capability, and Predictive Intervention Effectiveness, which most strongly influence downstream outcomes like learner engagement, risk identification, and instructional adaptability. These findings emphasize the strategic importance of upstream feedback, proactive risk planning, and advanced analytics in enhancing operational readiness. By bridging theoretical modelling, contemporary risk governance, and advanced learning analytics, this study offers a scalable framework for decision-making in complex, high-stakes education systems. The causal relationships revealed here provide a blueprint not only for optimizing military distance education but also for enhancing overall system resilience and adaptability in other critical domains. Full article

This paper introduces CodeDive, a web-based programming environment with real-time behavioral tracking designed to enhance student progress assessment and provide timely support for learners, while also addressing the academic integrity challenges posed by Large Language Models (LLMs). Visibility into the student’s learning process has become essential for effective pedagogical analysis and personalized feedback, especially in the era where LLMs can generate complete solutions, making it difficult to truly assess student learning and ensure academic integrity based solely on the final outcome. CodeDive provides this process-level transparency by capturing fine-grained events, such as code edits, executions, and pauses, enabling instructors to gain actionable insights for timely student support, analyze learning trajectories, and effectively uphold academic integrity. It operates on a scalable Kubernetes-based cloud architecture, ensuring security and user isolation via containerization and SSO authentication. As a browser-accessible platform, it requires no local installation, simplifying deployment. The system produces a rich data stream of all interaction events for pedagogical analysis. In a Spring 2025 deployment in an Operating Systems course with approximately 100 students, CodeDive captured nearly 25,000 code snapshots and over 4000 execution events with a low overhead. The collected data powered an interactive dashboard visualizing each learner’s coding timeline, offering actionable insights for timely student support and a deeper understanding of their problem-solving strategies. By shifting evaluation from the final artifact to the developmental process, CodeDive offers a practical solution for comprehensively assessing student progress and verifying authentic learning in the LLM era. The successful deployment confirms that CodeDive is a stable and valuable tool for maintaining pedagogical transparency and integrity in modern classrooms. Full article