Search Results (68)

Stroke often leads to severe motor impairment, especially in the upper limbs, greatly reducing a patient’s ability to perform daily tasks. Effective rehabilitation is essential to restore function and improve quality of life. Traditional therapies, while useful, may lack engagement, leading to low motivation and poor adherence. Gamification—using game-like elements in non-game contexts—offers a promising way to make rehabilitation more engaging. The authors explore a gamified rehabilitation system designed in Unity 3D using a Kinect V2 camera. The game includes key features such as adjustable difficulty, real-time and predominantly positive feedback, user friendliness, and data tracking for progress. The evaluations were conducted with 18 healthy participants, most of whom had prior virtual reality experience. About 77% found the application highly motivating. While the gameplay was well received, the visual design was noted as lacking engagement. Importantly, all users agreed that the game offers a broad range of difficulty levels, making it accessible to various users. The results suggest that the system has strong potential to improve rehabilitation outcomes and encourage long-term use through enhanced motivation and interactivity. Full article

Hand gestures are essential for interaction in augmented and virtual reality (AR/VR), allowing users to intuitively manipulate virtual objects and engage with human–machine interfaces (HMIs). Accurate gesture recognition is critical for effective task execution. However, users often encounter difficulties due to the lack of immediate and clear feedback from head-mounted displays (HMDs). Current tracking technologies cannot always guarantee reliable recognition, leaving users uncertain about whether their gestures have been successfully detected. To address this limitation, haptic feedback can play a key role by confirming gesture recognition and compensating for discrepancies between the visual perception of fingertip contact with virtual objects and the actual system recognition. The goal of this paper is to compare a simple vibrotactile ring with a full glove device and identify their possible improvements for a fundamental gesture like pinching and fine moving of objects using Microsoft HoloLens 2. Where the pinch action is considered an essential fine motor skill, augmented reality integrated with haptic feedback can be useful to notify the user of the recognition of the gestures and compensate for misaligned visual perception between the tracked fingertip with respect to virtual objects to determine better performance in terms of spatial precision. In our experiments, the participants’ median distance error using bare hands over all axes was 10.3 mm (interquartile range [IQR] = 13.1 mm) in a median time of 10.0 s (IQR = 4.0 s). While both haptic devices demonstrated improvement in participants precision with respect to the bare-hands case, participants achieved with the full glove median errors of 2.4 mm (IQR = 5.2) in a median time of 8.0 s (IQR = 6.0 s), and with the haptic rings they achieved even better performance with median errors of 2.0 mm (IQR = 2.0 mm) in an even better median time of only 6.0 s (IQR= 5.0 s). Our outcomes suggest that simple devices like the described haptic rings can be better than glove-like devices, offering better performance in terms of accuracy, execution time, and wearability. The haptic glove probably compromises hand and finger tracking with the Microsoft HoloLens 2. Full article

Background/Objectives: Rett Syndrome (RTT) is a rare neurodevelopmental disorder that affects girls and is characterized by severe motor and cognitive impairments, the loss of purposeful hand use, and communication difficulties. Children with RTT, especially those aged 5 to 9 years, often struggle to develop the foundational skills necessary for adaptive functioning, such as eye contact, object tracking, functional gestures, turn-taking, and basic communication. These abilities are essential for cognitive, social, and motor development and contribute to greater autonomy in daily life. This study aimed to explore the feasibility of a structured telerehabilitation program and to provide preliminary observations of its potential utility for young girls with RTT, addressing the presumed challenge of engaging this population in video-based interactive training. Methods: The intervention consisted of 30 remotely delivered sessions (each lasting 90 min), with assessments at baseline (A), after 5 weeks (B1), and after 10 weeks (B2). Quantitative outcome measures focused on changes in eye contact, object tracking, functional gestures, social engagement, and responsiveness to visual stimulus. Results: The findings indicate that the program was feasible and well-tolerated. Improvements were observed across all measured domains, and participants showed high levels of engagement and participation throughout the intervention. While these results are preliminary, they suggest that interactive digital formats may be promising for supporting foundational learning processes in children with RTT. Conclusions: This study provides initial evidence that telerehabilitation is a feasible approach for engaging young girls with RTT and supporting adaptive skill development. These findings may inform future research and the design of controlled studies to evaluate the efficacy of technology-assisted interventions in this population. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor symptoms, with tremors being one of the most prominent. Traditional assessment methods, such as the Unified Parkinson’s Disease Rating Scale (UPDRS), rely on subjective, intermittent evaluations, which can miss symptom fluctuations. This study presents the development and validation of the ELENA system, an IoT-based wearable device designed for the continuous monitoring of tremors in PD patients and medication tracking in PD patients. Named in honor of a 67-year-old woman who has lived with Parkinson’s since 2011 and inspired the project, the ELENA system integrates an MPU6050 accelerometer, an ESP32 microcontroller, and cloud-based data analysis and MATLAB. The ELENA system was calibrated and validated against an Apple Watch, demonstrating high accuracy with frequency deviations under 0.5% and an average percentage error of −0.37%. Unlike commercial devices, ELENA offers a clinical-grade solution with customizable data access and visualization tailored for healthcare providers. Participants, including PD patients and a non-PD control group, completed a series of clinical tasks to evaluate tremor monitoring capabilities. The results showed that the system effectively captured tremor frequency and amplitude, enabling the analysis of resting, action, and postural tremors. This study highlights the ELENA system’s potential to enhance PD management by providing real-time, remote monitoring of tremors. The scalable, cost-effective solution supports healthcare professionals in tracking disease progression and optimizing treatment plans, paving the way for improved patient outcomes. Full article

Background/Objectives: Eye-tracking technology has gained increasing attention in sports science, as it provides valuable insights into visual attention, decision-making, and motor planning. This systematic review examines the application of eye-tracking technology in basketball, highlighting its role in analyzing cognitive and perceptual strategies in players, referees, and coaches. Methods: A systematic search was conducted following PRISMA guidelines. Studies published up until December 2024 were retrieved from PubMed and Web of Science using keywords related to basketball, eye tracking, and visual search. The inclusion criteria focused on studies using eye-tracking technology to assess athletes, referees, and coaches. A total of 1706 articles were screened, of which 19 met the eligibility criteria. Results: Eye-tracking studies have shown that expert basketball players exhibit longer quiet eye (QE) durations and more efficient gaze behaviors compared to novices. In high-pressure situations, skilled players maintain more stable QE characteristics, leading to better shot accuracy. Referees rely on efficient gaze strategies to make split-second decisions, although less experienced referees tend to neglect key visual cues. In coaching, eye-tracking studies suggest that guided gaze techniques improve tactical understanding in novice players but have limited effects on experienced athletes. Conclusions: Eye tracking is a powerful tool for studying cognitive and behavioral functioning in basketball, offering valuable insights for performance enhancement and training strategies. Future research should explore real-game settings using mobile eye trackers and integrate artificial intelligence to further refine gaze-based training methods. Full article

The aging population has created a significant challenge affecting the world; social and healthcare systems need to ensure elderly individuals receive the necessary care services to improve their quality of life and maintain their independence. In response to this need, developing integrated digital solutions, such as IoT based wearable devices combined with artificial intelligence applications, offers a technological platform for creating Ambient Intelligence (AI) and Assisted Living (AAL) environments. These advancements can help reduce hospital admissions and lower healthcare costs. In this context, this article presents an IoT application based on MEMS (micro electro-mechanical systems) sensors integrated into a state-of-the-art microcontroller (STM55WB) for recognizing the movements of older individuals during daily activities. human activity recognition (HAR) is a field within computational engineering that focuses on automatically classifying human actions through data captured by sensors. This study has multiple objectives: to recognize movements such as grasping, leg flexion, circular arm movements, and walking in order to assess the motor skills of older individuals. The implemented system allows these movements to be detected in real time, and transmitted to a monitoring system server, where healthcare staff can analyze the data. The analysis methods employed include machine learning algorithms to identify movement patterns, statistical analysis to assess the frequency and quality of movements, and data visualization to track changes over time. These approaches enable the accurate assessment of older people’s motor skills, and facilitate the prompt identification of abnormal situations or emergencies. Additionally, a user-friendly technological solution is designed to be acceptable to the elderly, minimizing discomfort and stress associated with using technology. Finally, the goal is to ensure that the system is energy-efficient and cost-effective, promoting sustainable adoption. The results obtained are promising; the model achieved a high level of accuracy in recognizing specific movements, thus contributing to a precise assessment of the motor skills of the elderly. Notably, movement recognition was accomplished using an artificial intelligence model called Random Forest. Full article

The impact of ambient brightness surroundings on the peak velocities of visually guided saccades remains a topic of debate in the field of eye-tracking research. While some studies suggest that saccades in darkness are slower than in light, others question this finding, citing inconsistencies influenced by factors such as pupil deformation during saccades, gaze position, or the measurement technique itself. To investigate these, we developed RELAY (Robotic EyeLink AnalYsis), a low-cost, stepper motor-driven artificial eye capable of simulating human saccades with controlled pupil, gaze directions, and brightness. Using the EyeLink 1000, a widely employed eye tracker, we assessed accuracy and precision across three illumination settings. Our results confirm the reliability of the EyeLink 1000, demonstrating no artifacts in pupil-based eye tracking related to brightness variations. This suggests that previously observed changes in peak velocities with varying brightness are likely due to human factors, warranting further investigation. However, we observed systematic deviations in measured pupil size depending on gaze direction. These findings emphasize the importance of reporting illumination conditions and gaze parameters in eye-tracking experiments to ensure data consistency and comparability. Our novel artificial eye provides a robust and reproducible platform for evaluating eye tracking systems and deepening our understanding of the human visual system. Full article

Maintaining optimal levels of engagement during rehabilitation training is crucial for inducing neuroplasticity in the motor cortex, which directly influences positive rehabilitation outcomes. In this research article, we propose a virtual reality (VR) rehabilitation system that incorporates a steady-state visual evoked potential (SSVEP) paradigm to provide engagement feedback. The system utilizes a flickering target and cursor to detect the user’s engagement levels during a target-tracking task. Eighteen healthy participants were recruited to experience three experimental conditions: no feedback (NoF), performance feedback (PF), and neurofeedback (NF). Our results reveal significantly greater Mu suppression in the NF condition compared to the other conditions. However, no significant differences were observed in performance metrics, such as tracking error, among the three conditions. The amount of feedback between the PF and NF conditions also showed no substantial difference. These findings suggest the efficacy of our SSVEP-based engagement feedback paradigm in stimulating motor cortex activity during rehabilitation. Consequently, we conclude that neurofeedback, based on the user’s attentional state, proves to be more effective in promoting motor cortex activation and facilitating neuroplastic changes. This research highlights the potential of integrating VR rehabilitation with an engagement feedback system for successful rehabilitation training. Full article

Background: Examining visual behavior during a motor learning paradigm can enhance our understanding of how infants learn motor skills. The aim of this study was to determine if infants who learned a contingency visually anticipated the outcomes of their behavior. Methods: 15 infants (6–9 months of age) participated in a contingency learning paradigm. When an infant produced a right leg movement, a robot provided reinforcement by clapping. Three types of visual gaze events were identified: predictive, reactive, and not looking. An exploratory analysis examined the trends in visual-motor behavior that can be used to inform future questions and practices in contingency learning studies. Results: All classically defined learners visually anticipated robot activation at greater than random chance (W = 21; p = 0.028). Specifically, all but one learners displayed a distribution of gaze timing identified as predictive (skewness: 0.56–2.42) with the median timing preceding robot activation by 0.31 s (range: −0.40–0.18 s). Conclusions: Findings suggest that most learners displayed visual anticipation withing the first minutes of performing the paradigm. Further, the classical definition of learning a contingency paradigm in infants can be sharpened to further the design of contingency learning studies and advance the processes infants use to learn motor skills. Full article

Background/Objectives: The incorporation of biomechanics into stroke neurorehabilitation may serve to strengthen the effectiveness of rehabilitation strategies by increasing our understanding of human movement and recovery processes. The present bibliometric analysis of biomechanics research in stroke neurorehabilitation is conducted with the objectives of identifying influential studies, key trends, and emerging research areas that would inform future research and clinical practice. Methods: A comprehensive bibliometric analysis was performed using documents retrieved from the Scopus database on 6 August 2024. The analysis included performance metrics such as publication counts and citation analysis, as well as science mapping techniques, including co-authorship, bibliographic coupling, co-citation, and keyword co-occurrence analyses. Data visualization tools such as VOSviewer and Power BI were utilized to map the bibliometric networks and trends. Results: An overabundance of recent work has yielded substantial advancements in the application of brain–computer interfaces to electroencephalography and functional neuroimaging during stroke neurorehabilitation., which translate neural activity into control signals for external devices and provide critical insights into the biomechanics of motor recovery by enabling precise tracking and feedback of movement during rehabilitation. A sampling of the most impactful contributors and influential publications identified two leading countries of contribution: the United States and China. Three prominent research topic clusters were also noted: biomechanical evaluation and movement analysis, neurorehabilitation and robotics, and motor recovery and functional rehabilitation. Conclusions: The findings underscore the growing integration of advanced technologies such as robotics, neuroimaging, and virtual reality into neurorehabilitation practices. These innovations are poised to enhance the precision and effectiveness of therapeutic interventions. Future research should focus on the long-term impacts of these technologies and the development of accessible, cost-effective tools for clinical use. The integration of multidisciplinary approaches will be crucial in optimizing patient outcomes and improving the quality of life for stroke survivors. Full article

This paper presents a novel approach to predicting shot accuracy in badminton by analyzing Quiet Eye (QE) metrics such as QE duration, fixation points, and gaze dynamics. We develop a neural network model that combines visual data from eye-tracking devices with biomechanical data such as body posture and shuttlecock trajectory. Our model is designed to predict shot accuracy, providing insights into the role of QE in performance. The study involved 30 badminton players of varying skill levels from the Chinese Swimming Club in Singapore. Using a combination of eye-tracking technology and motion capture systems, we collected data on QE metrics and biomechanical factors during a series of badminton shots for a total of 750. Key results include: (1) The neural network model achieved 85% accuracy in predicting shot outcomes, demonstrating the potential of integrating QE metrics with biomechanical data. (2) QE duration and onset were identified as the most significant predictors of shot accuracy, followed by racket speed and wrist angle at impact. (3) Elite players exhibited significantly longer QE durations (M = 289.5 ms) compared to intermediate (M = 213.7 ms) and novice players (M = 168.3 ms). (4) A strong positive correlation (r = 0.72) was found between QE duration and shot accuracy across all skill levels. These findings have important implications for badminton training and performance evaluation. The study suggests that QE-based training programs could significantly enhance players’ shot accuracy. Furthermore, the predictive model developed in this study offers a framework for real-time performance analysis and personalized training regimens in badminton. By bridging cognitive neuroscience and sports performance through advanced data analytics, this research paves the way for more sophisticated, individualized training approaches in badminton and potentially other fast-paced sports. Future research directions include exploring the temporal dynamics of QE during matches and developing real-time feedback systems based on QE metrics. Full article

Background: Stroke is the third leading cause of disability. Virtual reality (VR) has shown promising results in post-stroke rehabilitation. The VR TRAVEE system was designed for the neuromotor rehabilitation of the upper limb after a stroke and offers the ability to track limb movements by providing auditory feedback and visual augmentation. The World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0), aligned with the International Classification of Functioning, Disability, and Health (ICF) principles, is a valid tool for measuring disability regardless of its cause. This study aimed to investigate the feasibility of the VR TRAVEE system in upper limb rehabilitation for stroke patients. Methods: A total of 14 stroke patients with residual hemiparesis were enrolled in the study. They underwent a 10-day program combining conventional therapy (CnvT) with VR rehabilitation. At baseline (T0), the upper limb was assessed using the Modified Ashworth Scale (MAS), active range of motion (AROM), and the Numeric Rating Scale (NRS) for pain. These assessments were repeated after the 10-day rehabilitation program (T1). Additionally, disability was measured using WHODAS 2.0 at T0 and again 30 days after completing the program. Results: Significant improvements were observed in AROM and MAS scores for the shoulder, elbow, wrist, and metacarpophalangeal joints, as well as in the reduction in shoulder pain (p ˂ 0.001). WHODAS scores decreased across all six domains, with a statistically significant improvement in the Cognition domain (p = 0.011). Conclusions: Combining CnvT with VR as a rehabilitation approach enhances motor function in the upper limb. This method has the potential to reduce disability scores and promote neuroplasticity. Full article

A typical orchard’s mechanical operation consists of three or four stages: lining and digging for plantation, moving the seedling from nurseries to the farm, moving the seedling to the planting hole, and planting the seedling in the hole. However, the digging of the planting hole is the most time-consuming operation. In fruit orchards, the use of robots is increasingly becoming more prevalent to increase operational efficiency. They offer practical and effective services to both industry and people, whether they are assigned to plant trees, reduce the use of chemical fertilizers, or carry heavy loads to relieve staff. Robots can operate for extended periods of time and can be highly adept at repetitive tasks like planting many trees. The present study aims to identify the locations for planting trees in orchards using geographic information systems (GISs), to develop an autonomous drilling machine and use the developed robot to open planting holes. There is no comparable study on autonomous hole planting in the literature in this regard. The agricultural mobile robot is a four=wheeled nonholonomic robot with differential steering and forwarding capability to stable target positions. The designed mobile robot can be used in fully autonomous, partially autonomous, or fully manual modes. The drilling system, which is a y-axis shifter driven by a DC motor with a reducer includes an auger with a 2.1 HP gasoline engine. SOLIDWORKS 2020 software was used for designing and drawing the mobile robot and drilling system. The Microsoft Visual Basic.NET programming language was used to create the robot navigation system and drilling mechanism software. The cross-track error (XTE), which determines the distances between the actual and desired holes positions, was utilized to analyze the steering accuracy of the mobile robot to the drilling spots. Consequently, the average of the arithmetic means was determined to be 4.35 cm, and the standard deviation was 1.73 cm. This figure indicates that the suggested system is effective for drilling plant holes in orchards. Full article

Cerebral palsy is a disorder of central motor and postural development, resulting in limited mobility. Cerebral palsy is often accompanied by cognitive impairment and abnormal behavior, significantly impacting individuals and society. Time, energy, and economic investment in the rehabilitation process is substantial, yet the rehabilitation outcomes often remain unsatisfactory. Additionally, some patients have limited sensory perception during rehabilitation training, making it challenging to effectively regulate exercise intensity. Traditional evaluation methods are mostly based on recovery performance, lack guidance at the neurophysiological level, and have an unequal distribution of medical rehabilitation resources, which pose great challenges to the rehabilitation of patients. Based on the issues mentioned above, this paper proposes a real-time cerebral signal monitoring system based on wearable devices. This system can monitor and store blood oxygen, heart rate, myoelectric, and EEG signals during cerebral palsy rehabilitation, and it can track and monitor signals during the rehabilitation treatment process. The system includes two parts: hardware design and software design. The hardware design includes a data signal acquisition module, a main control chip (ESP32), a muscle electrical sensor module, a brain electrical sensor module, a blood/heart rate acquisition module, etc. It is primarily for real-time signal data acquisition, processing, and uploading to the cloud server. The software design includes functions such as data receiving, data processing, data storage, network configuration, and remote communication and enables the visual monitoring of data signals. The system can achieve real-time monitoring of electromyography, electroencephalography, and blood oxygen levels, as well as the heart rate of patients with cerebral palsy, and adjust rehabilitation training in real-time during the rehabilitation process. At the same time, based on the real-time storage of the original electromyography and electroencephalography data, it can provide auxiliary guidance for later rehabilitation evaluation and effective data support for the entire rehabilitation treatment process. Full article

The recent availability of commercial platforms for behavioral analyses in zebrafish larvae based on video-tracking technologies has exponentially increased the number of studies analyzing different behaviors in this model organism to assess neurotoxicity. Among the most commonly used assays in zebrafish larvae are basal locomotor activity (BLA) and visual motor responses (VMRs). However, the effect of different intrinsic and extrinsic factors that can significantly alter the outcome of these assays is still not well understood. In this work, we have analyzed the influence of age (5–8 days post-fertilization), time of day (8:00, 10:00, 12:00, 14:00; 16:00, 18:00, and 20:00 h), and experiment (three experiments performed at different days) on BLA and VMR results (4004 analyses for each behavior) in 143 larvae. The results from both behaviors were adjusted to a random-effects linear regression model using generalized least squares (GLSs), including in the model the effect of the three variables, the second-way interactions between them, and the three-way interaction. The results presented in this manuscript show a specific effect of all three intrinsic factors and their interactions on both behaviors, supporting the view that the most stable time period for performing these behavioral assays is from 10:00 am to 04:00 pm, with some differences depending on the age of the larva and the behavioral test. Full article