Search Results (327)

This study presents a comprehensive analysis of Speed Climbing athletes by examining motion parameters critical to elite performance. As such, several key values are extracted from about 900 competition recordings in order to generate a dataset for the identification of patterns in athletes’ technique and efficiency. A CNN-based framework is used to automate the detection of human keypoints and features, enabling a large-scale evaluation of climbing dynamics. The results revealed significant variations in performance for single sections of the wall, particularly in relation to start reaction times (with differences of up to 0.27 s) and increased split times the closer the athletes are to the end of the Speed Climbing wall (from 0.39 s to 0.45 s). In addition, a more detailed examination of the movement sequences was carried out by analyzing the velocity trajectories of hands and feet. The results showed that coordinated and harmonic movements, especially of the lower limbs, correlate strongly with the performance outcome. To ensure an individualized view of the data points, a comparison was made between multiple athletes, revealing insights into the influence of individual biomechanics on the efficiency of movements. The findings provide both trainers and athletes with interesting insights in relation to tailoring training methods by including split time benchmarks and limb coordination. Full article

This paper provides a comprehensive review of the structural features and biomechanical functions of modern passive and semi-active knee prostheses, followed by comparative analysis. Based on findings from scientific literature and engineering practice, a new conceptual knee prosthesis was developed using a modular design approach. The proposed structure was modeled in SolidWorks, and its kinematic behavior and structural integrity were quantitatively evaluated through finite element analysis (FEA). The knee module was specifically designed to integrate with previously developed ankle and foot prosthetic components via an adapter interface. This modular approach allows the prosthesis to be configured according to the individual clinical needs of the patient. Simulation results confirmed that the proposed design meets the requirements for motion accuracy and structural reliability. In future work, the physical prototype will be manufactured using 3D printing with PLA plastic as an initial test material, followed by fabrication with high-strength engineering plastics or metal alloys. This study represents a critical early step toward the development of a fully functional, adaptive lower-limb prosthetic system. Full article

Ergonomic risk assessment is crucial for preventing work-related musculoskeletal disorders (WMSDs), which often arise from repetitive tasks, prolonged sitting, and load handling, leading to absenteeism and increased healthcare costs. Biomechanical risk assessment, such as RULA/REBA, is increasingly being enhanced by camera-based motion capture systems, either marker-based (MBSs) or markerless systems (MCBSs). This systematic review compared MBSs and MCBSs regarding accuracy, validity, and reliability for industrial ergonomic risk analysis. A comprehensive search of PubMed, WoS, ScienceDirect, IEEE Xplore, and PEDro (31 May 2025) identified 898 records; after screening with PICO-based eligibility criteria, 20 quantitative studies were included. Methodological quality was assessed with the COSMIN Risk of Bias tool, synthesized using PRISMA 2020, and graded with EBRO criteria. MBSs showed the highest precision (0.5–1.5 mm error) and reliability (ICC > 0.90) but were limited by cost and laboratory constraints. MCBSs demonstrated moderate-to-high accuracy (5–20 mm error; mean joint-angle error: 2.31° ± 4.00°) and good reliability (ICC > 0.80), with greater practicality in field settings. Several studies reported strong validity for RULA/REBA prediction (accuracy up to 89%, κ = 0.71). In conclusion, MCBSs provide a feasible, scalable alternative to traditional ergonomic assessment, combining reliability with usability and supporting integration into occupational risk prevention. Full article

Background/Objectives: In recent years, 3D shape models of the human body have been used for various purposes. In principle, CT and MRI tomographic images are necessary to create such models. However, CT imaging and MRI generally impose heavy physical and financial burdens on the person being imaged, the model creator, and the hospital where the imaging facility is located. To reduce these burdens, the purpose of this study was to propose a method of creating individually adapted models by using simple X-ray images, which provide relatively little information and can therefore be easily acquired, and by transforming an existing base model. Methods: From medical images, anatomical feature values and scanning feature values that use the points that compose the contour line that can represent the shape of the femoral knee joint area were acquired, and deformed by free-form deformation. Free-form deformations were automatically performed to match the feature values using optimization calculations based on the confidence region method. The accuracy of the deformed model was evaluated by the distance between surfaces of the deformed model and the node points of the reference model. Results: Deformation and evaluation were performed for 13 cases, with a mean error of 1.54 mm and a maximum error of 12.88 mm. In addition, the deformation using scanning feature points was more accurate than the deformation using anatomical feature points. Conclusions: This method is useful because it requires only the acquisition of feature points from two medical images to create the model, and overall average accuracy is considered acceptable for applications in biomechanical modeling and motion analysis. Full article

Multiple sclerosis (MS) is a chronic disease of the central nervous system that significantly impairs gait and mobility, contributing to a high risk of falls, reduced participation in daily activities, and diminished quality of life. Despite existing interventions such as exercise programs and pharmacological treatments, challenges such as fatigue, pain, and limited accessibility underscore the need for alternative therapies. Focal vibration therapy (FVT) has shown promise in improving gait, reducing spasticity, and enhancing mobility in people with MS (PwMS). However, further research is required to evaluate its long-term feasibility and optimize its parameters. This study examined the feasibility and preliminary efficacy of a home-based four-week wearable FVT device on gait and explored how FVT parameters impact gait and mobility outcomes. In this pilot double-blind randomized controlled trial, 22 PwMS were randomized into control and vibration groups (four FVT groups with varying vibration intensities/durations). Participants wore Myovolt^® vibrators on distal quadricep muscles near the rectus femoris insertion (approximately 2 cm from the medial edge of the patella), gastrocnemius/soleus, and tibialis anterior muscles (10 min/muscle, 3 days/week, 4 weeks). Feasibility was evaluated via adherence and satisfaction (QUEST 2.0, interviews). Gait (3D motion analysis) and mobility (T25FW) were assessed at baseline and post-intervention. Data were analyzed using descriptive/inferential statistics and thematic analysis. Of 22 participants, 17 completed post-intervention (16 intervention, 1 control). Wearable FVT showed promising feasibility, with high satisfaction despite minor adjustability issues. Intervention groups improved gait speed (p = 0.014), stride length (p = 0.004), and ankle angle (p = 0.043), but T25FW was unchanged (p > 0.05). High-intensity FVT enhanced knee/hip moments. This study’s results support the feasibility of wearable FVT for home-based management of mobility symptoms in MS with high participant satisfaction and acceptance. Notable gains in gait parameters suggest FVT’s potential to enhance neuromuscular control and proprioception but may be insufficient to lead to mobility improvements. Subgroup analyses highlighted the impact of vibration intensity and duration on knee joint mechanics, emphasizing the need for personalized dosing strategies. Challenges included participant retention in the control group and burdensome biomechanical assessments, which will be addressed in future studies through improved sham devices and a larger sample size. Full article

Objectives. The present study aimed to quantify changes in spinal kinematics during Nordic walking compared to regular walking (RW) for 60 s on a training path among physically fit young males (n = 20, aged 19–22 years). Methods. Two walking speeds were analyzed: 3 km/h and 5 km/h. The experimental setup was designed to assess spinal angular rotations using five kinematic parameters: upper spine, lower spine, thoracic region, lumbar region, and pelvis. Results. The data were acquired from 9 compact inertial sensors and the following motion analysis is carried out using 3D MioMotion IMU sensor’s analysis system. The differences in the obtained cyclic biomechanical parameters were detected using functional data analysis (FDA) statistical tests. Conclusions. The key finding of the study is that Nordic walking significantly alters the angular kinematic pattern of spinal movement as it revealed significant differences in all five measured parameters when compared to normal walking. Notably, the most pronounced changes were observed in the upper spine and pelvis motion. Additionally, Nordic walking increased stance phase duration and velocity: (i) significantly increased the duration of the stance phase in all three planes of motion; (ii) significantly increased the velocity during the stance phase across all three planes. These reported findings highlight the biomechanical, preventive, therapeutic, and rehabilitative potential of Nordic walking. Full article

Background/Objectives: This study aimed to identify the functional adaptations that enable competitive performance in a Paralympic cyclist with optimized bilateral transtibial prostheses compared to a conventional cyclist. Additionally, it describes the development of the prosthesis, designed through a user-centered engineering process incorporating Quality Function Deployment (QFD), Computer-Aided Design (CAD), Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), and topological optimization, with the final design (Design 1.4) achieving optimal structural integrity, aerodynamic efficiency, and anatomical fit. Methods: Both athletes performed a progressive cycling test with 50-watt increments every three minutes until exhaustion. Cardiorespiratory metrics, lactate thresholds, and joint kinematics were assessed. Results: Although the conventional cyclist demonstrated higher Maximal Oxygen Uptake (VO₂max) and anaerobic threshold, the Paralympic cyclist exceeded 120% of his predicted VO₂max, had a higher Respiratory Exchange Ratio (RER) [1.32 vs. 1.11], and displayed greater joint ranges of motion with lower trunk angular variability. Lactate thresholds were similar between athletes. Conclusions: These findings illustrate, in this specific case, that despite lower aerobic capacity, the Paralympic cyclist achieved comparable performance through efficient biomechanical and physiological adaptations. Integrating advanced prosthetic design with individualized evaluation appears essential to optimizing performance in elite adaptive cycling. Full article

Low back pain (LBP) remains one of the most prevalent and disabling musculoskeletal conditions globally, with profound social, economic, and healthcare implications. The rising incidence and chronic nature of LBP highlight the need for more objective, personalized, and effective approaches to assessment and rehabilitation. In this context, bioengineering has emerged as a transformative field, offering novel tools and methodologies that enhance the understanding and management of LBP. This narrative review examines current bioengineering applications in both diagnostic and therapeutic domains. For assessment, technologies such as wearable inertial sensors, three-dimensional motion capture systems, surface electromyography, and biomechanical modeling provide real-time, quantitative insights into posture, movement patterns, and muscle activity. On the therapeutic front, innovations including robotic exoskeletons, neuromuscular electrical stimulation, virtual reality-based rehabilitation, and tele-rehabilitation platforms are increasingly being integrated into multimodal treatment protocols. These technologies support precision medicine by tailoring interventions to each patient’s biomechanical and functional profile. Furthermore, the incorporation of artificial intelligence into clinical workflows enables automated data analysis, predictive modeling, and decision support systems, while future directions such as digital twin technology hold promise for personalized simulation and outcome forecasting. While these advancements are promising, further validation in large-scale, real-world settings is required to ensure safety, efficacy, and equitable accessibility. Ultimately, bioengineering provides a multidimensional, data-driven framework that has the potential to significantly improve the assessment, rehabilitation, and overall management of LBP. Full article

Marker-based motion capture systems using infrared cameras (IR MoCaps) are commonly employed in biomechanical research. However, their high costs pose challenges for many institutions seeking to implement such systems. This study aims to develop a neural network (NN) model to estimate the digitized coordinates of spherical markers and to establish a lower-cost marker-based motion capture system using RGB cameras. Thirteen participants were instructed to walk at self-selected speeds while their movements were recorded with eight RGB cameras. Each participant undertook trials with 24 mm spherical markers attached to 25 body landmarks (marker trials), as well as trials without markers (non-marker trials). To generate training data, virtual markers mimicking spherical markers were randomly inserted into images from the non-marker trials. These images were then used to fine-tune a pre-trained model, resulting in an NN model capable of detecting spherical markers. The digitized coordinates inferred by the NN model were employed to reconstruct the three-dimensional coordinates of the spherical markers, which were subsequently compared with the gold standard. The mean resultant error was determined to be 2.2 mm. These results suggest that the proposed method enables fully automatic marker reconstruction comparable to that of IR MoCap, highlighting its potential for application in motion analysis. Full article

The assessment of upper limb (UL) movement patterns plays a critical role in the rehabilitation of individuals with motor impairments resulting from neuromotor disorders, which significantly affect essential activities of daily living (ADLs) such as drinking and eating. However, conventional clinical evaluation methods often lack objective and quantitative insights into the biomechanics of movement. To enable accurate identification of pathological patterns, it is first necessary to establish normative biomechanical and electrophysiological benchmarks in healthy individuals. In this study, a previously developed, low-cost, wearable, and portable prototype device was employed to objectively assess UL movement. The system, specifically designed for clinical applicability, integrates surface electromyography (EMG) sensors and an inertial measurement unit (IMU) to capture muscle activity and kinematic data, respectively. Thirty healthy participants were recruited to perform standardized drinking and eating tasks. The analysis focused on characterizing muscle activation patterns and joint range of motion during different task phases. Results revealed consistent variations in muscle contraction and joint kinematics, allowing the identification of distinct activation profiles for key shoulder muscles. The findings contribute to the establishment of a normative dataset that can serve as a reference for the assessment of clinical populations. Such data are essential for informing rehabilitation strategies and developing predictive models of UL function during ADLs in individuals with neuromotor disorders. Full article

Objectives: This study investigates the biomechanics of the bench press and overhead press exercises by modeling the trunk and upper limbs as a kinematic chain of rigid links connected by revolute joints and actuated by single- and two-joint muscles, with motion constrained by the barbell. The aims were to (i) assess the different contributions of shoulder and elbow torques during lifting, (ii) identify the parameters influencing joint loads, (iii) explain the origin of the sticking region, and (iv) validate the model against experimental barbell kinematics. Methods: Equations of motion and joint reaction forces were derived analytically in closed form. Dynamic simulations produced vertical barbell velocity profiles under various conditions. A waveform similarity analysis was used to compare simulated profiles with experimental data from maximal bench press trials. Results: The sticking region occurred when shoulder torque dropped below a critical threshold, resulting in a local velocity minimum. Adding elbow torque reduced this dip and shifted the velocity minimum from 38 cm to 23 cm above the chest, although it prolonged the time needed to overcome it. Static analysis revealed that grip width and barbell constraint had a greater effect on shaping the sticking region than muscle architecture parameters. Elbow extensors contributed minimally during early lift phases but became dominant near full extension. Model predictions showed high similarity to experimental data in the pre-sticking (SI = 0.962, p = 0.028) and sticking (SI = 0.949, p = 0.014) phases, with reduced, non-significant similarity post-sticking (SI = 0.881, p > 0.05) due to the assumption of constant torques. Conclusions: The model offers biomechanical insight into how joint torques and barbell constraints shape movement. The findings support training strategies that target shoulder strength early in the lift and elbow strength near lockout to minimize sticking and improve performance. Full article

Aim: This scoping review examines the application of artificial intelligence (AI) in sports biomechanics, with a focus on enhancing performance and preventing injuries. The review addresses key research questions, including primary AI methods, their effectiveness in improving athletic performance, their potential for injury prediction, sport-specific applications, strategies for translating knowledge, ethical considerations, and remaining research gaps. Following the PRISMA-ScR guidelines, a comprehensive literature search was conducted across five databases (PubMed/MEDLINE, Web of Science, IEEE Xplore, Scopus, and SPORTDiscus), encompassing studies published between January 2015 and December 2024. After screening 3248 articles, 73 studies met the inclusion criteria (Cohen’s kappa = 0.84). Data were collected on AI techniques, biomechanical parameters, performance metrics, and implementation details. Results revealed a shift from traditional statistical models to advanced machine learning methods. Based on moderate-quality evidence from 12 studies, convolutional neural networks reached 94% agreement with international experts in technique assessment. Computer vision demonstrated accuracy within 15 mm compared to marker-based systems (6 studies, moderate quality). AI-driven training plans showed 25% accuracy improvements (4 studies, limited evidence). Random forest models predicted hamstring injuries with 85% accuracy (3 studies, moderate quality). Learning management systems enhanced knowledge transfer, raising coaches’ understanding by 45% and athlete adherence by 3.4 times. Implementing integrated AI systems resulted in a 23% reduction in reinjury rates. However, significant challenges remain, including standardizing data, improving model interpretability, validating models in real-world settings, and integrating them into coaching routines. In summary, incorporating AI into sports biomechanics marks a groundbreaking advancement, providing analytical capabilities that surpass traditional techniques. Future research should focus on creating explainable AI, applying rigorous validation methods, handling data ethically, and ensuring equitable access to promote the widespread and responsible use of AI across all levels of competitive sports. Full article

This study presents a simulation-based feasibility analysis of a beam steering metasurface, theoretically driven by mechanical energy harvested from human motion via a triboelectric nanogenerator (TENG). In the proposed model, the TENG converts biomechanical motion into alternating current (AC), which is rectified into direct current (DC) to bias varactor diodes integrated into each metasurface unit cell. These bias voltages are numerically applied to dynamically modulate the local reflection phase, enabling beam steering without external power. Full-wave electromagnetic simulations were conducted to confirm the feasibility of beam manipulation under TENG-generated voltage levels. The proposed simulation-driven design offers a promising framework for battery-free, adaptive electromagnetic control with potential applications in wearable electronics, intelligent sensing, and energy-autonomous radar systems. Full article

This study presents an advanced simulation-based methodology for evaluating the dynamic vibrational behavior of the operator–vehicle assembly in autonomous electric agricultural tractors. Using the TE-0 electric tractor as the experimental platform, the research is structured into three integrated stages. In the first stage, a seated anthropometric virtual model of the human operator is developed based on experimental data and biomechanical validation. The second stage involves a detailed modal analysis of the TE-0 electric tractor using Altair Sim Solid, with the objective of determining the natural frequencies and vibration modes in the [0–80] Hz range, in compliance with ISO 2631-1. This analysis captures both the structural-induced frequencies—associated with the chassis, wheelbase, and metallic frame—and the operational-induced frequencies, influenced by the velocity and terrain profile. Subsequently, the modal analysis of the “Grammer Cabin Seat” is conducted to assess its dynamic response and identify critical vibration modes, highlighting how the seat behaves under vibrational stimuli from the tractor and terrain. The third stage extends the analysis to the virtual operator model seated on the tractor seat, investigating the biomechanical response of the human body and the operator–seat–vehicle interaction during simulated motion. Simulations were carried out using SolidWorks 2023 and Altair Sim Solid over a frequency range of [0–80] Hz, corresponding to operation on unprocessed soil covered with grass, at a constant forward speed of 7 km/h. The results reveal critical resonance modes and vibration transmission paths that may impact operator health, comfort, and system performance. The research contributes to the development of safer, more ergonomic, and sustainable autonomous agricultural transport systems. By simulating real-world operation scenarios and integrating a rigorously validated experimental protocol—including vibration data acquisition, biomechanical modeling, and multi-stage modal analysis—this study demonstrates the importance of advanced modeling in optimizing system-level performance, minimizing harmful vibrations, and supporting the transition toward resilient and eco-efficient electric tractor platforms in smart agricultural mobility. Full article

The objective of this study is to classify the types of boxing punches using machine learning algorithms that processed skin-strain time history measurements from a self-adhesive, elastic fabric, wearable sensor called Motion Tape. A human participant study was designed to capture movements during boxing training. Subjects were asked to perform multiple sets of punches during the entire test, which consisted of jabs and hooks with and without striking a heavy bag. The collected Motion Tape data was used to train and compare time series classification algorithms to identify the types of punches performed and associated conditions. The results demonstrated that Motion Tape, in combination with machine learning techniques, could effectively classify different punch types based on skin-strain measurements. These findings highlighted the potential of the system as an effective tool for human performance analysis in sports and biomechanics applications. Full article