Search Results (180)

We presented a biomimetic approach to designing robot-to-human handover of objects in a collaborative assembly task. We developed a human–robot hybrid cell where a human and a robot collaborated with each other to perform the assembly operations of a product in a flexible manufacturing setup. Firstly, we investigated human psychology and biomechanics (kinetics and kinematics) for human-to-robot handover of an object in the human–robot collaborative set-up in three separate experimental conditions: (i) human possessed high trust in the robot, (ii) human possessed moderate trust in the robot, and (iii) human possessed low trust in the robot. The results showed that human psychology was significantly impacted by human trust in the robot, which also impacted the biomechanics of human-to-robot handover, i.e., human hand movement slowed down, the angle between human hand and robot arm increased (formed a braced handover configuration), and human grip forces increased if human trust in the robot decreased, and vice versa. Secondly, being inspired by those empirical results related to human psychology and biomechanics, we proposed a novel robot-to-human object handover mechanism (strategy). According to the novel handover mechanism, the robot varied its handover configurations and motions through kinematic redundancy with the aim of reducing potential impulse forces on the human body through the object during the handover when robot trust in the human was low. We implemented the proposed robot-to-human handover mechanism in the human–robot collaborative assembly task in the hybrid cell. The experimental evaluation results showed significant improvements in human–robot interaction (HRI) in terms of transparency, naturalness, engagement, cooperation, cognitive workload, and human trust in the robot, and in overall performance in terms of handover safety, handover success rate, and assembly efficiency. The results can help design and develop human–robot handover mechanisms for human–robot collaborative tasks in various applications such as industrial manufacturing and manipulation, medical surgery, warehouse, transport, logistics, construction, machine shops, goods delivery, etc. Full article

Background: Advancements in low-cost additive manufacturing and artificial intelligence have enabled new avenues for developing accessible myoelectric prostheses. However, achieving reliable real-time control and ensuring mechanical durability remain significant challenges, particularly for affordable systems designed for resource-constrained settings. Objective: This study aimed to design and validate a low-cost, 3D-printed prosthetic arm that integrates single-channel electromyography (EMG) sensing with machine learning for real-time gesture classification. The device incorporates an anatomically inspired structure with 14 passive mechanical degrees of freedom (DOF) and 5 actively actuated tendon-driven DOF. The objective was to evaluate the system’s ability to recognize open, close, and power-grip gestures and to assess its functional grasping performance. Method: A Fast Fourier Transform (FFT)-based feature extraction pipeline was implemented on single-channel EMG data collected from able-bodied participants. A Support Vector Machine (SVM) classifier was trained on 5000 EMG samples to distinguish three gesture classes and benchmarked against alternative models. Mechanical performance was assessed through power-grip evaluation, while material feasibility was examined using PLA-based 3D-printed components. No amputee trials or long-term durability tests were conducted in this phase. Results: The SVM classifier achieved 92.7% accuracy, outperforming K-Nearest Neighbors and Artificial Neural Networks. The prosthetic hand demonstrated a 96.4% power-grip success rate, confirming stable grasping performance despite its simplified tendon-driven actuation. Limitations include the reliance on single-channel EMG, testing restricted to able-bodied subjects, and the absence of dynamic loading or long-term mechanical reliability assessments, which collectively limit clinical generalizability. Overall, the findings confirm the technical feasibility of integrating low-cost EMG sensing, machine learning, and 3D printing for real-time prosthetic control while emphasizing the need for expanded biomechanical testing and amputee-specific validation prior to clinical application. Full article

Background/Objectives: This study investigated biomechanical differences between right-handed (RHPs) and left-handed (LHPs) youth baseball players by analyzing shoulder and elbow range of motion (ROM), muscle strength, and humeral torsion. Side-to-side asymmetries were also examined to identify potential handedness-related adaptations. Methods: This cross-sectional study included 2008 youth baseball players (1829 RHPs and 179 LHPs) aged 9–13 years; female players were excluded because of their small number, and only male participants were analyzed. Shoulder and elbow ROM, muscle strength, and humeral torsion were evaluated, with humeral torsion data collected from 1024 measurements (946 RHPs, 78 LHPs). Group differences were analyzed using the Mann–Whitney U and Wilcoxon Signed-Rank tests. Logistic regression analysis identified independent factors associated with being an LHP, while Pearson correlation analyses explored the relationships between humeral torsion and external/internal rotation. Results: LHPs exhibited significantly larger nondominant shoulder external rotation (p < 0.001), dominant internal rotation (p = 0.003), dominant shoulder horizontal adduction (p = 0.007), dominant elbow flexion (p = 0.006), and side-to-side prone internal rotation strength ratio (p < 0.001). LHPs also showed smaller dominant shoulder external rotation (p = 0.012), nondominant shoulder internal rotation (p = 0.001), nondominant horizontal adduction (p = 0.037), dominant prone external rotation strength (p = 0.002), and humeral torsion (p = 0.031). Humeral torsion differences correlated with external rotation in LHPs (r = 0.236) and internal rotation in RHPs (r = −0.153). Predictors of left-handedness included lower dominant shoulder external rotation (OR = 0.937) and higher dominant elbow flexion (OR = 1.410). Conclusions: This study provides novel insights into the normal functional characteristics of LHPs, an area that has been relatively underexplored. These findings serve as a basis for future studies on risk assessment, injury prevention, and performance optimization in youth baseball players. Full article

In recent years, agricultural production activities have been advancing towards mechanization and intelligence to bridge the growing gap between the high labor intensity and time sensitivity of harvesting operations and the limited labor resources. As the component that directly interacts with target crops, the end-effector is a crucial part of agricultural harvesting robots. This paper first reviews their materials, number of fingers, actuation methods, and detachment techniques. Analysis reveals that three-fingered end-effectors, known for their stability and ease of control, are the most prevalent. Soft materials have gained significant attention due to their flexibility and low-damage characteristics, while the emergence of variable stiffness technology holds promise for addressing their issues of poor stability and fragility. The introduction of bionics and composite concepts offers potential for enhancing the performance of end-effectors. Subsequently, starting from an analysis of the biomechanical properties of fruits and vegetables, the relationship between mechanical damage and the intrinsic parameters of produce is elucidated. On the other hand, practical and efficient finite element analysis has been applied to various stages of end-effector research, such as structural design and grasping force estimation. Given the importance of compliance control, this paper explores the current research status of various control methods. It emphasizes that while hybrid force–position control often suffers from frequent controller switching, which directly affects real-time performance, active admittance control and impedance control directly convert external forces or torques into the robot’s reference position and velocity, resulting in more stable and flexible external control. To enable a unified comparison of end-effector performance, this review proposes a progressive comparison framework centered on control philosophy, comprising the ontological characteristic layer, physical interaction layer, feedback optimization layer, and task layer. Additionally, in response to the current lack of scientific rigor and systematization in performance evaluation systems for end-effectors, performance evaluation criteria (harvest success rate, harvest time, and damage rate) are defined to standardize the characterization of end-effector performance. Finally, this paper summarizes the challenges faced in the development of end-effectors and analyzes their causes. It highlights how emerging technologies, such as digital twin technology, can improve the control accuracy and flexibility of end-effectors. Full article

Objectives: The application of well-controlled, quantitative measurement systems has challenged the traditional notion that rigidity in Parkinson’s disease (PD) is a velocity-independent phenomenon. This review aimed to evaluate whether rigidity in PD is velocity-dependent or velocity-independent across different joints, body regions, testing speeds, and methodologies. Methods: This systematic review followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Methodological quality of cross-sectional studies was assessed using the Appraisal Tool for Cross-Sectional Studies (AXIS), and reporting completeness was evaluated with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist. Results: Seventeen studies were included and analyzed by the body part assessed (wrist, elbow, hand, knee, trunk). Rigidity quantification in PD used various biomechanical technologies, sometimes combined with neurophysiological methods. Although rigidity is classically considered velocity-independent, experimental evidence suggests a more complex behavior, partially velocity-dependent, especially at moderate to high angular velocities. Methodological quality was variable but generally acceptable, with more recent studies showing stronger adherence to AXIS. However, compliance with STROBE reporting standards remained inconsistent. Conclusions: While rigidity in PD has not been classically defined as velocity-dependent, current evidence indicates that, under specific testing conditions, rigidity increases with passive movement velocity. These findings challenge traditional clinical assumptions and emphasize the need for standardized measurement protocols. Full article

High-precision analyses of baseball pitching have traditionally relied on optical motion capture systems, which, despite their accuracy, are complex and impractical for widespread use. Classifying sequential pitching phases, essential for biomechanical evaluation, conventionally requires manual expert labeling, a time-consuming and labor-intensive process. Accurate identification of phase boundaries is critical because they correspond to key temporal events related to pitching injuries. This study developed and validated a smartphone-based system for automatically classifying the five key pitching phases—wind-up, stride, arm-cocking, arm acceleration, and follow-through—using pose estimation artificial intelligence and machine learning. Slow-motion videos (240 frames per second, 1080p) of 500 healthy right-handed high school pitchers were recorded from the front using a single smartphone. Skeletal landmarks were extracted using MediaPipe, and 33 kinematic features, including joint angles and limb distances, were computed. Expert-annotated phase labels were used to train classification models. Among the models evaluated, Light Gradient Boosting Machine (LightGBM) achieved a classification accuracy of 99.7% and processed each video in a few seconds demonstrating feasibility for on-site analysis. This system enables high-accuracy phase classification directly from video without motion capture, supporting future tools to detect abnormal pitching mechanics, prevent throwing-related injuries, and broaden access to pitching analysis. Full article

With the increasing number of patients with Parkinson’s disease, the detection of Parkinson’s disease is crucial for the early intervention and treatment of this condition. The motor characteristics of Parkinson’s disease primarily include typical motor features. Flexible pressure sensor arrays, due to their unique mechanical properties and biocompatibility, have shown great potential for capturing movement characteristics. This research aims to develop a deep learning model based on foot pressure data for the detection of Parkinson’s disease. By collecting the pressure data of patients during walking and analyzing the distribution of foot pressure, the model can capture the unique biomechanical characteristics of Parkinson’s disease patients. To address the core challenges of spatial irregularity and data disorder in footprint data, we propose an innovative approach that leverages the Transformer-based attention mechanism and tensor fusion technique to enable accurate identification of Parkinson’s disease. This attention mechanism has inherent permutation invariance, which is highly suitable for the feature learning of footprint data. The tensor fusion technique can effectively integrate the foot features at different levels. A large-scale dataset of foot pressure data was used for training and validation. The experimental results show that the model achieves a high accuracy of 87.03% and good stability in Parkinson’s disease detection, enabling effective differentiation between patients and healthy individuals. On the one hand, our work is critical for analyzing pressure data and fusion features from large-area flexible force-sensitive sensors, which enables the accurate identification of foot data. On the other hand, it greatly facilitates gait analysis, gait evaluation, and the diagnosis of Parkinson’s disease. Full article

Background: Children with spastic hand impairments resulting from cerebral palsy or neuromuscular disorders often exhibit a restricted range of motion and diminished functional use. Rehabilitation devices that assist joint mobilization can enhance therapeutic outcomes, yet few solutions target pediatric populations. Methods: This study aimed to design, implement, and preliminarily evaluate a biomechanical device tailored to promote flexo-extension, radial–ulnar deviation, and supination movements in spastic hands of school-aged children. A prototype combining a motor-driven actuation system, adjustable wrist and finger supports, and a MATLAB-based graphical user interface was developed. Two participants (aged 8 and 10) with clinically diagnosed spastic hemiparesis underwent 25-minute sessions over 15 consecutive days. Joint angles were recorded before and after each session using an electro-goniometer. Data normality was assessed via the Shapiro–Wilk test, and pre–post differences were analyzed with the Wilcoxon signed-rank test (α = 0.05). Results: Both participants demonstrated consistent increases in their active range of motion across all measured planes. Median flexo-extension improved by 12.5° (p = 0.001), ulnar–radial deviation by 7.3° (p = 0.002), and supination by 9.1° (p = 0.001). No adverse events occurred, and device tolerance remained high throughout the intervention. Conclusions: The device facilitated statistically significant enhancements in joint mobility in a small pediatric cohort, supporting its feasibility and safety in spastic hand rehabilitation. These preliminary findings warrant larger controlled trials to confirm the device’s efficacy, optimize treatment protocols, and assess its long-term functional benefits. Full article

Background: In spinal fusion surgery, intersomatic compression force is currently applied subjectively by the operating surgeon, despite its critical role on implant stability and risk of subsidence. No standardized measurement or guideline exists to control or quantify the amount of force applied. Methods: In a two-phase exploratory study, we evaluated whether proprioceptive muscle memory allows reliable reproduction of applied manual compression forces. In Phase 1, 30 participants applied force to a compression clamp equipped with a strain gauge, simulating spinal interbody compression on a 3D-printed vertebral model. They were then asked to reproduce this force using a hand dynamometer at defined time intervals. In Phase 2, intraoperative compression forces applied during spinal fusion procedures were retrospectively assessed by having the operating surgeon reproduce the force on a dynamometer. Results: Participants were able to reproduce their initial manual compression force within a 15% deviation, even 15 min after the initial application. In 116 clinical cases, an average compression force of 146.3 ± 18.5 N was recorded. No significant differences were observed across different spinal segments. Conclusions: These findings provide initial data toward defining a reproducible reference range for indirect intraoperative compression assessment. Standardization of applied force may help improve biomechanical outcomes and reduce complications such as implant migration, pseudarthrosis, or cage subsidence. Full article

Cell phone use has become ubiquitous in everyday life for many, yet the potential long-term impacts on hand biomechanics remain unknown. A review was performed on the topic of handheld device use and biomechanics of the hand to identify common findings as well as gaps in the literature. A literature search was performed using several databases and a comprehensive search strategy using controlled keywords was designed. A total of 1556 studies were screened, and 28 studies examining handheld device use were included. A total of 2173 individuals participated in the included studies where cell phone (n = 23) and tablet (n = 5) usage were examined, focusing on the kinematics (n = 17), muscles (n = 13), joints (n = 2), nerves (n = 4), and tendons (n = 1) of the hand. Handheld device use placed the thumb carpometacarpal (CMC) and metacarpophalangeal (MCP) joints in extreme positions of abduction, as well as wrist extension and ulnar deviation. Increased muscle activity of the first dorsal interossei, extensor digitorum communis, and abductor pollicis brevis was demonstrated while using a handheld cellular device. Studies also suggested that handheld device use is powered by the thumb CMC and MCP joints, as well as intrinsic musculature. Thus, individuals could consider operating handheld devices with a two-hand grip, minimizing device size/weight, or using the index finger or voice texting to decrease muscular fatigue and offload joints. Further studies should be conducted to evaluate the long-term effects of cell phone use on the hand and wrist. Full article

Stroke often leads to persistent upper limb dysfunction that impairs activities of daily living, yet objective biomechanical indicators for precise assessment remain limited. This study aimed to characterize phase-specific impairments in energy output, torque stability, and muscle coordination during the hand-to-mouth (HTM) task and to explore their potential for improving rehabilitation evaluation. Motion data from 20 stroke patients and 20 healthy controls were recorded using wearable surface electromyography and inertial measurement unit systems. A musculoskeletal model was applied to calculate joint torque, mechanical work, torque smoothness, and a novel torque-based co-contraction index across four movement subphases. These phase-specific metrics demonstrated significant correlations with clinical motor impairment scores, confirming their clinical validity. Significant dynamic features were then selected to construct machine learning models for group classification. Stroke patients showed reduced output capacity, increased torque fluctuations, and abnormal co-contraction patterns that varied across subphases. Among the classifiers, the quadratic support vector machine achieved the best performance, with an accuracy of 84.6% and an AUC of 0.853, surpassing models based on whole-task features. These findings demonstrate that phase-specific biomechanical features sensitively capture neuromuscular deficits in stroke survivors and highlight the potential of phase-specific biomechanics to inform future individualized rehabilitation assessment and treatment planning. Full article

Awareness regarding the welfare of captive baboons is rising. Consequently, the best possible medical care is offered to injured animals. To this purpose, knowledge of the species-specific anatomy is a prerequisite. However, detailed anatomical reference works on this species, such as overviews or atlases, are sparse. The existing anatomical literature is scattered in often outdated works or elaborates on a specific detail. Veterinarians responsible for the medical care of captive baboons, therefore, habitually rely on human anatomical atlases. As overviews of the baboon joint morphology are particularly sparse, this first study in a series of three aims to provide a comprehensive overview of the arthrology of the thoracic limb of the hamadryas baboon (Papio hamadryas). The several synovial joints present in the shoulder region, elbow region, and hand are included. Not only the typical connective tissue elements that form the joints but also the associated muscle tendons are depicted. The osseous structures to which these components attach are identified as well. Standard veterinary terminology is used, complemented by human anatomical nomenclature where the former falls short. High-resolution color photographs support the text, allowing this work to serve not only as a dissection guide for veterinary and academic use but also as a baseline for clinical medical care and future research in primate morphology and biomechanics. Full article

Virtual communication, involving the transmission of all human senses, is the next step in the development of telecommunications. Achieving this vision requires real-time data exchange with low latency, which in turn necessitates the implementation of the Tactile Internet (TI). TI will ensure the transmission of high-quality tactile data, especially when combined with audio and video signals, thus enabling more realistic interactions in virtual environments. In this context, advances in realism increasingly depend on the accurate simulation of the grasping process and hand–object interactions. To address this, in this paper, we methodically present the challenges of human–object interaction in virtual environments, together with a detailed review of the datasets used in grasping modeling and the integration of physics-based and machine learning approaches. Based on this review, we propose a multi-step framework that simulates grasping as a series of biomechanical, perceptual, and control processes. The proposed model aims to support realistic human interaction with virtual objects in immersive settings and to enable integration into applications such as remote manipulation, rehabilitation, and virtual learning. Full article

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 study presents a real-time hand tracking and collision detection system for immersive mixed-reality boxing training on Apple Vision Pro (Apple Inc., Cupertino, CA, USA). Leveraging the device’s advanced spatial computing capabilities, this research addresses the limitations of traditional fitness applications that lack precision for technique-based sports like boxing with visual-only hand tracking. The system is designed to provide objective feedback by recognizing boxing-specific gestures with sub-centimeter accuracy and validating biomechanical correctness during punch execution. A three-stage pipeline consisting of geometric filtering, biomechanical validation, and punch technique assessment rejects accidental or improper motions. Experimental evaluation involving 12 participants demonstrated a gesture recognition accuracy of 96.3% and a technique validation accuracy of 88.5%. The system consistently operated at 60 FPS with low latency and high robustness across diverse lighting conditions. These results indicate the potential of Apple Vision Pro as a platform for precision sports training and highlight the educational impact of mixed reality in democratizing access to high-quality boxing instruction. The proposed framework is extensible to other skill-based sports requiring fine motor control and real-time feedback. Full article