Search Results (106)

In recent years, the realization of a society in which humans and robots coexist has become highly anticipated. As a result, robots are expected to exhibit versatility regardless of their operating environments, along with high responsiveness, to ensure safety and enable dynamic task execution. To meet these demands, we design a comprehensive system composed of two primary components: high-speed skeleton tracking and path planning. For tracking, we implement a high-speed skeleton tracking method that combines deep learning-based detection with optical flow-based motion extraction. In addition, we introduce a dynamic search area adjustment technique that focuses on the target joint to extract the desired motion more accurately. For path planning, we propose a high-speed, geometry-informed potential field model that addresses four key challenges: (P1) avoiding local minima, (P2) suppressing oscillations, (P3) ensuring adaptability to dynamic environments, and (P4) handling obstacles with arbitrary 3D shapes. We validated the effectiveness of our high-frequency feedback control and the proposed system through a series of simulations and real-world collision-free path planning experiments. Our high-speed skeleton tracking operates at 250 Hz, which is eight times faster than conventional deep learning-based methods, and our path planning method runs at over 10,000 Hz. The proposed system offers both versatility across different working environments and low latencies. Therefore, we hope that it will contribute to a foundational motion generation framework for human–robot collaboration (HRC), applicable to a wide range of downstream tasks while ensuring safety in dynamic environments. Full article

Researchers have extensively explored learning analytics in online courses, primarily focusing on linear course structures where students progress sequentially through lessons and assessments. However, non-linear courses, which allow students to complete tasks in any order, present unique challenges for learning analytics due to the variability in course progression among students. This study proposes a method for applying learning analytics to non-linear, self-paced MOOC-style courses, addressing early performance prediction and online learning pattern detection. The novelty of our approach lies in introducing a personalized feature aggregation that adapts to each student’s progress rather than being defined at fixed timelines. We evaluated three types of features—engagement, behavior, and performance—using data from a non-linear large-scale Moodle course designed to prepare high school students for a public university entrance exam. Our approach predicted early student performance, achieving an F1-score of 0.73 at a 20% cumulative weight assessment. Feature importance analysis revealed that performance and behavior were the strongest predictors, while engagement features, such as time spent on educational resources, also played a significant role. In addition to performance prediction, we conducted a clustering analysis that identified four distinct online learning patterns recurring across various cumulative weight assessments. These patterns provide valuable insights into student behavior and performance and have practical implications, enabling educators to deliver more personalized feedback and targeted interventions to meet individual student needs. Full article

This paper presents a comprehensive user study aimed as assessing and differentiating operator expertise within bilateral teleoperation systems. The primary objective is to identify key performance metrics that effectively distinguish novice from expert users. Unlike prior approaches that focus primarily on psychological evaluations, this study emphasizes direct performance analysis across a range of telerobotic tasks. Ten participants (six novices and four experts) were assessed based on task completion time and difficulty, error rates, manipulator motion characteristics, gaze behaviour, and subjective feedback via questionnaires. The results show that experienced operators outperformed novices by completing tasks faster, making fewer errors, and demonstrating smoother manipulator control, as reflected by reduced jerks and higher spatial precision. Also, experts maintained consistent performance even as task complexity increased, whereas novices experienced a sharp decline, particularly at higher difficulty levels. Questionnaire responses further revealed that novices experienced higher mental and physical demands, especially in unfamiliar tasks, while experts demonstrated higher concentration and arousal levels. Additionally, the study introduces gaze transition entropy (GTE) and stationary gaze entropy (SGE) metrics to quantify visual attention strategies, with experts exhibiting more focused, goal-oriented gaze patterns, while novices showed more erratic and inefficient behaviour. These findings highlight both quantitative and qualitative measures as critical for evaluating operator performance and informing future teleoperation training programs. Full article

This study presents the design, development, and evaluation of a multifunctional stepladder that integrates four functionalities: a stepladder, Pilates chair, wheelchair, and walking aid. Unlike existing research that focuses on single-function assistive devices, this study uniquely integrates a stepladder, wheelchair, walking aid, and Pilates chair into one multifunctional device, offering a compact, space-saving solution that addresses multiple daily needs in a single design. Building upon previous research, which conceptualized a multifunctional stepladder by synthesizing ideas, features, and functions from patent literature, existing products, and scientific articles, this study focuses on the design and testing phases to refine and validate the concept. Using sustainable materials like mild steel and aluminium, the design was optimized through structural simulations, ensuring durability under loads of up to 100 kg. Usability tests revealed that the invention significantly reduced task completion times, saved five times the space compared to single-function products, and provided enhanced versatility. Cost analysis highlighted its affordability, with a retail price of MYR 1392—approximately 35% lower than the combined cost of its single-function counterparts. Participant feedback noted strengths such as eco-friendliness, practicality, and ergonomic design, alongside areas for improvement, including portability, armrests, and storage. Future work includes enhanced portability for stair navigation, outdoor usability tests, and integration of smart technologies. This multifunctional stepladder significantly contributes to caregivers by reducing the physical burden of managing multiple assistive devices, enhancing efficiency in daily caregiving tasks, and providing a safer, more convenient tool that supports both mobility and exercise for elderly users. This multifunctional stepladder also offers a sustainable, cost-effective, and user-centric solution, addressing usability gaps while supporting global sustainability and accessibility initiatives. Full article

Background: Care plans are a critical tool in nursing education because they enhance clinical competence; however, undergraduate students often face challenges in writing them effectively, which can impact their readiness for clinical practice. While existing research predominantly focuses on care plans within specific clinical contexts, little is known about how students experience the learning process and how these experiences shape their professional development. Objectives: This study aimed to explore the experiences of undergraduate nursing students in writing care plans to understand the impact on their clinical competence and identify strategies for improvement. Design: A qualitative phenomenological study utilizing focus group interviews was conducted. Methods: Semi-structured interviews with open-ended questions were conducted with 15 undergraduate nursing students in six focus groups. Data were analyzed using Colaizzi’s method to identify key themes. Results: Four main themes emerged: (1) enhancement and integration of knowledge and skills, (2) initiative learning and motivation, (3) adequate support and feedback from tutors, and (4) difficulties in transitioning from classroom learning to clinical practice. The findings highlight that care plan writing enhances students’ competence in patient care, with positive learning attitudes and tutor feedback playing crucial roles. However, students encounter difficulties in applying theoretical knowledge to complex clinical scenarios, particularly in prioritizing interventions and managing time effectively. Conclusions: Writing care plans not only fosters personal and professional development but also enhances students’ clinical competence, preparing them for real-world practice. Nurse tutors are encouraged to promote consistent practice in care plan writing, provide timely feedback, and share clinical experiences to support students’ learning. These findings underscore the need to reframe care plans as developmental tools rather than mere tasks for clinical transition, ultimately enhancing the quality of patient care. Full article

This study introduces a new methodology for an Inference Index (InI) called the Inference Index In Testing Model Effectiveness methodology (INFINITE), aiming to evaluate the performance of Large Language Models (LLMs) in code generation tasks. The InI index provides a comprehensive assessment focusing on three key components: efficiency, consistency, and accuracy. This approach encapsulates time-based efficiency, response quality, and the stability of model outputs, offering a thorough understanding of LLM performance beyond traditional accuracy metrics. We apply this methodology to compare OpenAI’s GPT-4o (GPT), OpenAI-o1 pro (OAI1), and OpenAI-o3 mini-high (OAI3) in generating Python code for two tasks: a data-cleaning and statistical computation task and a Long Short-Term Memory (LSTM) model generation task for forecasting meteorological variables such as temperature, relative humidity, and wind speed. Our findings demonstrate that GPT outperforms OAI1 and performs comparably to OAI3 regarding accuracy and workflow efficiency. The study reveals that LLM-assisted code generation can produce results similar to expert-designed models with effective prompting and refinement. GPT’s performance advantage highlights the benefits of widespread use and user feedback. These findings contribute to advancing AI-assisted software development, providing a structured approach for evaluating LLMs in coding tasks and setting the groundwork for future studies on broader model comparisons and expanded assessment frameworks. Full article

The rapid urbanization of recent decades has intensified climate change challenges, demanding sophisticated solutions to build resilient and sustainable cities. A key aspect of sustainable urban planning is decentralizing and democratizing its processes, which requires citizen involvement from the early design stages. While current solutions such as digital tools, participatory workshops, gamification, and social media can enhance participation, they often exclude non-experts or those lacking digital skills. To address these limitations, this manuscript proposes a VR/AR gamified solution using open-source software and open GIS data. Specifically, it investigates the euPOLIS game as an innovative participatory tool offering an alternative to traditional approaches. This game decentralizes urban planning by shifting technical tasks to experts while citizens engage interactively, focusing solely on proposing solutions. To explore the potential of the proposed methodology, the euPOLIS game was demonstrated as a workshop activity in TNOC 2024 Festival, where 30 individuals from different academic background (i.e., citizens, architects, planners, etc.) voluntarily engaged and provided their impressions and feedback. The findings suggest that gamified solutions such as serious/simulation AR/VR games can effectively promote co-design, co-participation, and co-creation in urban planning in an inclusive and engaging manner. Full article

Background: Artificial Intelligence (AI)-driven training systems are becoming increasingly important in surgical education, particularly in the context of laparoscopic suturing. This systematic review aims to assess the impact of AI on skill acquisition, long-term retention, and clinical performance, with a specific focus on the types of machine learning (ML) techniques applied to laparoscopic suturing training and their associated advantages and limitations. Methods: A comprehensive search was conducted across multiple databases, including PubMed, IEEE Xplore, Cochrane Library, and ScienceDirect, for studies published between 2005 and 2024. Following the PRISMA guidelines, 1200 articles were initially screened, and 33 studies met the inclusion criteria. This review specifically focuses on ML techniques such as deep learning, motion capture, and video segmentation and their application in laparoscopic suturing training. The quality of the included studies was assessed, considering factors such as sample size, follow-up duration, and potential biases. Results: AI-based training systems have shown notable improvements in the laparoscopic suturing process, offering clear advantages over traditional methods. These systems enhance precision, efficiency, and long-term retention of key suturing skills. The use of personalized feedback and real-time performance tracking allows learners to gain proficiency more rapidly and ensures that skills are retained over time. These technologies are particularly beneficial for novice surgeons and provide valuable support in resource-limited settings, where access to expert instructors and advanced equipment may be scarce. Key machine learning techniques, including deep learning, motion capture, and video segmentation, have significantly improved specific suturing tasks, such as needle manipulation, insertion techniques, knot tying, and grip control, all of which are critical to mastering laparoscopic suturing. Conclusions: AI-driven training tools are reshaping laparoscopic suturing education by improving skill acquisition, providing real-time feedback, and enhancing long-term retention. Deep learning, motion capture, and video segmentation techniques have proven most effective in refining suturing tasks such as needle manipulation and knot tying. While AI offers significant advantages, limitations in accuracy, scalability, and integration remain. Further research, particularly large-scale, high-quality studies, is necessary to refine these tools and ensure their effective implementation in real-world clinical settings. Full article

This research focused on the organizational level of teamwork in companies in the Slovak Republic. The study helped to reveal the possibilities of sustainable management of team cooperation. Utilizing a mixed-methods approach, including quantitative questionnaires and qualitative interviews, the study examined four key areas: (1) team system and work positions in the team, (2) division of tasks in the team and tasks management, (3) team communication (external, internal), and (4) team training activities. The findings reveal a widespread use of teamwork, with a significant proportion of tasks performed by teams and many organizations exhibiting high reliance on teamwork. However, the study also highlights a dominance of traditional management practices, where team formation is primarily driven by capacity constraints, with limited support for organic team emergence. Moreover, management often retains significant control over team decision-making. The research further indicates that many employees lack adequate training in teamwork skills and principles. While teamwork is prevalent, reliance on traditional communication methods, such as face-to-face meetings and phone calls, persists, despite the potential of ICT tools to enhance collaboration. Furthermore, the effective use of ICT tools is hindered by challenges such as incompatibility between different systems and limited data accessibility. These findings underscore the need for organizations to embrace more agile and flexible team structures, invest in comprehensive teamwork training for all employees, leverage ICT tools effectively to improve communication and collaboration, and foster a culture of continuous improvement and feedback within teams. By addressing these areas, organizations can enhance teamwork effectiveness, improve employee engagement, and ultimately achieve better organizational outcomes so that they can implement sustainable approaches for organizing team collaboration. Full article

According to the World Health Organization, the global population is aging, with cognitive and memory functions declining from the age of 40–50. Individuals aged 65 and older are particularly prone to dementia. Therefore, we developed an interactive system for visual cognitive training to prevent dementia and delay the onset of memory loss. The system comprises three “three-dimensional objects” with printed 2D barcodes and near-field communication (NFC) tags and operating software processing text, images, and multimedia content. Electroencephalography (EEG) data from a brainwave sensor were used to interpret brain signals. The system operates through interactive games combined with real-time feedback from EEG data to reduce the likelihood of dementia. The system provides feedback based on textual, visual, and multimedia information and offers a new form of entertainment. Thirty participants were invited to participate in a pre-test questionnaire survey. Different tasks were assigned to randomly selected participants with three-dimensional objects. Sensing technologies such as quick-response (QR) codes and near-field communication (NFC) were used to display information on smartphones. Visual content included text-image narratives and media playback. EEG was used for visual recognition and perception responses. The system was evaluated using the system usability scale (SUS). Finally, the data obtained from participants using the system were analyzed. The system improved hand-eye coordination and brain memory using interactive games. After receiving visual information, brain function was stimulated through brain stimulation and focused reading, which prevents dementia. This system could be introduced into the healthcare industry to accumulate long-term cognitive function data for the brain and personal health data to prevent the occurrence of dementia. Full article

Growth mindset, which asserts that intelligence and abilities can be cultivated through effort and learning, has garnered substantial attention in psychological and educational research. While the psychological and behavioral impacts of growth mindset are well-established, the underlying neural mechanisms remain relatively underexplored. Furthermore, there is a lack of comprehensive reviews synthesizing the neural evidence on growth mindset, hindering a fuller understanding of this concept. This scoping review aims to synthesize existing empirical studies on the neural mechanisms of growth mindset, focusing on research objectives, methods, and participant characteristics. A total of 15 studies were reviewed, revealing six primary research objectives: (1) neural mechanisms of error and feedback processing, (2) domain-specific mindsets, (3) neural changes resulting from mindset interventions, (4) mindsets and grit, (5) the neuroanatomy of mindsets, and (6) neural mechanisms of stereotype violation, with error and feedback processing being the most frequently investigated. Ten of the 15 studies employed EEG, while other techniques included structural MRI, task-based fMRI, and resting-state fMRI, with the majority of research focusing on adult populations. Although the existing literature offers valuable insights, further research is needed to explore additional aspects of mindsets, particularly in children, and to refine the methodologies used to investigate the neural mechanisms underlying growth mindset. Full article

The Mobility Intensive Training (Mob-IT) protocol is an innovative intervention focused on motor learning to improve the mobility of children with cerebral palsy (CP). The objective was to describe the feasibility and intervention fidelity of Mob-IT. A single-subject experimental study was conducted with four children with CP, a median age of 11 (7–13) years, and a Gross Motor Function Classification System I–III. The Mob-IT included 24 h of practice of mobility goals, delivered three times a week in 2 h sessions over four weeks. Feasibility was assessed using the Qualitative Feedback Questionnaire (QFQ), evaluating adherence, acceptability, adverse effects, the clarity of procedures, and intervention time. The Canadian Occupational Performance Measure (COPM) was used to assess participant and caregiver satisfaction. Fidelity was measured by the type of feedback provided (intrinsic vs. extrinsic), task challenge level, and intervention volume. Participants reported good acceptance, few adverse effects, and satisfaction with the outcomes. The intervention adhered to the proposed principles, with a focus on extrinsic feedback and tasks showing progression over time. Time was well spent, being 78% focused on activities and using mostly extrinsic-focused feedback. The Mob-IT protocol was considered feasible and faithful to its principles. As this is a feasibility study, the results indicate the need to expand the intervention to a larger, randomized study. Full article

Extended reality (XR) technologies—encompassing virtual reality (VR), augmented reality (AR), and mixed reality (MR)—are transforming cognitive assessment and training by offering immersive, interactive environments that simulate real-world tasks. XR enhances ecological validity while enabling real-time, multimodal data collection through tools such as galvanic skin response (GSR), electroencephalography (EEG), eye tracking (ET), hand tracking, and body tracking. This allows for a more comprehensive understanding of cognitive and emotional processes, as well as adaptive, personalized interventions for users. Despite these advancements, current XR applications often underutilize the full potential of multimodal integration, relying primarily on visual and auditory inputs. Challenges such as cybersickness, usability concerns, and accessibility barriers further limit the widespread adoption of XR tools in cognitive science and clinical practice. This review examines XR-based cognitive assessment and training, focusing on its advantages over traditional methods, including ecological validity, engagement, and adaptability. It also explores unresolved challenges such as system usability, cost, and the need for multimodal feedback integration. The review concludes by identifying opportunities for optimizing XR tools to improve cognitive evaluation and rehabilitation outcomes, particularly for diverse populations, including older adults and individuals with cognitive impairments. Full article

Federated learning (FL) utilizes distributed data processing to enable collaborative machine learning model development while safeguarding user privacy. However, the decentralized nature of FL, combined with data heterogeneity, substantially expands the attack surface for backdoor threats. Existing FL attack and defense strategies typically target the entire model, neglecting the critical backdoor parameters—a small subset of parameters that govern model vulnerabilities. Focusing on these parameters can replicate the impact of attacking the entire model while greatly reducing the risk of detection by advanced defenses. To address this challenge, we introduce Key Parameter Backdoor Attack in Federated Learning (KPBAFL), an innovative, adaptive, and scalable framework specifically designed to exploit model vulnerabilities by targeting critical backdoor parameters. KPBAFL integrates three core components: key parameter analysis, a beacon feedback mechanism, and adaptive attack strategies. By embedding beacons within the backdoor model, the framework can gather real-time attack feedback and dynamically adjust its strategy accordingly. When these components operate in concert, KPBAFL exhibits exceptional stealthiness, achieving an attack success rate (ASR) exceeding 96.5% while maintaining a benign task accuracy (BTA) of 97.8% across various datasets and models. Extensive experiments demonstrate its effectiveness, even in the presence of advanced defenses such as FLAME, Fldetector, Rflbat, and Deepsight, underscoring its strong generalizability. Although the modular design ensures adaptability, the framework’s performance may significantly degrade if the components are not properly synchronized. Our research provides a critical foundation for understanding and mitigating backdoor vulnerabilities in federated learning systems. Full article

This positioning paper explores integrating smart in-process inspection and human–automation symbiosis within human–cyber–physical manufacturing systems. As manufacturing environments evolve with increased automation and digitalization, the synergy between human operators and intelligent systems becomes vital for optimizing production performance. Human–automation symbiosis, a vision widely endorsed as the future of human–automation research, emphasizes closer partnership and mutually beneficial collaboration between human and automation agents. In addition, to maintain high product quality and enable the in-time feedback of process issues for advanced manufacturing, in-process inspection is an efficient strategy that manufacturers adopt. In this regard, this paper outlines a research framework combining smart in-process inspection and human–automation symbiosis, enabling real-time defect identification and process optimization with cognitive intelligence. Smart in-process inspection studies the effective automation of real-time inspection and defect mitigation using data-driven technologies and intelligent agents to foster adaptability in complex production environments. Concurrently, human–automation symbiosis focuses on achieving a symbiotic human–automation relationship through cognitive task allocation and behavioral nudges to enhance human–automation collaboration. It promotes a human-centered manufacturing paradigm by integrating the studies in advanced manufacturing systems, cognitive engineering, and human–automation interaction. This paper examines critical technical challenges, including defect inspection and mitigation, human cognition modeling for adaptive task allocation, and manufacturing nudging design and personalization. A research roadmap detailing the technical solutions to these challenges is proposed. Full article