Search Results (64)

The zygomaticus major and minor (ZMa/ZMi) are key determinants of smile dynamics and midface contour, yet they exhibit substantial morphological variability—including bifid or multibellied bellies, accessory slips, and atypical insertions. Such variants can alter force vectors, fat-compartment boundaries, and SMAS planes, increasing the risk of asymmetry, contour irregularities, or “joker smile” following facelifts, fillers, thread lifts, and smile reconstruction. To our knowledge, this is the first review to integrate the Landfald classification of ZMa/ZMi variants with a standardized dynamic imaging-based workflow for aesthetic and reconstructive midface procedures. We conducted a narrative literature synthesis of anatomical and imaging studies. Bifid or multibellied variants have been reported in up to 35% of cadaveric specimens. We synthesize anatomical, biomechanical, and imaging evidence (MRI, dynamic US, 3D analysis) to propose a practical protocol: (1) focused history and dynamic examination, (2) US/EMG mapping of contraction vectors, (3) optional high-resolution MRI for complex cases, and (4) individualized adjustment of surgical vectors, injection planes, and dosing. Procedure-specific adaptations are outlined for deep-plane releases, thread-lift trajectories, filler depth selection, and muscle-transfer orientation. We emphasize that standardizing preoperative dynamic mapping and adopting a “patient-specific mimetic profile” can enhance safety, predictability, and preservation of authentic expression, ultimately improving patient satisfaction across diverse midface interventions. Full article

Objective: The aim of this study was to examine how transcranial electrical stimulation (tES) modulates intermuscular coherence (IMC) in sprinters and develop an interpretable neural network model for performance prediction. Methods: Thirty elite sprinters completed a randomized crossover trial involving three tES conditions: motor cortex stimulation (C1/C2), prefrontal stimulation (F3), and sham. Sprint performance metrics (0–100 m phase analysis) and lower-limb sEMG signals were collected. A Kolmogorov–Arnold Network (KAN) was trained to decode neuromuscular coordination–sprint performance relationships using IMC and time–frequency sEMG features. Results: Motor cortex tDCS increased 30–60 m sprint velocity by 2.2% versus sham (p < 0.05, η² = 0.25). γ-band IMC in key muscle pairs (rectus femoris–biceps femoris, tibialis anterior–gastrocnemius) significantly heightened under motor cortex stimulation (F > 4.2, p < 0.03). The KAN model achieved high predictive accuracy (R² = 0.83) through cross-validation, with derived symbolic equations mapping neuromuscular features to performance. Conclusions: Targeted tDCS enhances neuromuscular coordination and sprint velocity, while KAN provides a transparent framework for performance modeling in elite sports. Full article

Classifying study objects into groups is facilitated by fuzzy classifiers based on a set of rules and membership functions. Typically, the characteristics of the study objects are used to establish the criteria for classification. This work arises from the need to design fuzzy classifiers in contexts where real data is scarce or highly random, proposing a design based on statistics and chaotic maps that simplifies the design process. This study introduces the development of a fuzzy classifier, assuming that three features of the population to be classified are random variables. A Mamdani fuzzy inference system and three pseudorandom number generators based on one-dimensional chaotic maps are utilized to achieve this. The logistic, Bernoulli, and tent chaotic maps are implemented to emulate the random features of the target population, and their statistical distribution functions serve as input to the fuzzy inference system. Four experimental tests were conducted to demonstrate the functionality of the proposed classifier. The results show that it is possible to achieve a symmetric and robust classification through simple adjustments to membership functions, without the need for supervised training, which represents a significant methodological contribution, especially because this indicates that designers with minimal experience can build effective classifiers in just a few steps. Real applications of the proposed design may focus on the classification of biomedical signals (sEMG), network traffic, and personalized medical assistance systems, where data exhibits high variability and randomness. Full article

To address the limitations of traditional mirror therapy in stroke rehabilitation, such as rigid movement mapping and insufficient personalization, this study proposes a robot-assisted mirror rehabilitation framework integrating multimodal biofeedback. By synchronously capturing kinematic features of the unaffected upper limb and surface electromyography (sEMG) signals from the affected limb, a dual-modal feature fusion network based on a cross-attention mechanism is developed. This network dynamically generates a time-varying mirror ratio coefficient $\lambda$, which is incorporated into the exoskeleton’s admittance control loop. Combining a trajectory generation algorithm based on dynamic movement primitives (DMPs) with a compliant control strategy incorporating dynamic constraints, the system achieves personalized rehabilitation trajectory planning and safe interaction. Experimental results demonstrate that, compared to traditional mirror therapy, the proposed system exhibits superior performance in bilateral trajectory covariance metrics, the mirror symmetry index, and muscle activation levels. Full article

To enhance robotic adaptability in dynamic environments, this study proposes a multimodal framework for skill transfer. The framework integrates vision-based kinesthetic teaching with surface electromyography (sEMG) signals to estimate human impedance. We establish a Cartesian-space model of upper-limb stiffness, linearly mapping sEMG signals to end-point stiffness. For flexible task execution, dynamic movement primitives (DMPs) generalize learned skills across varying scenarios. An adaptive admittance controller, incorporating sEMG-modulated stiffness, is developed and validated on a UR5 robot. Experiments involving elastic-band stretching demonstrate that the system successfully transfers human impedance characteristics to the robot, enhancing stability, environmental adaptability, and safety during physical interaction. Full article

Eye tracking and EMG are novel measurement technologies that can be used to assess perceptual processes in sports under real-life conditions. The study was conducted on two groups of soccer goalkeepers (N = 60): Group A—expert goalkeepers (22.8 ± 2.15 years of age; training experience 12.77 ± 3.89 years); Group B—novice goalkeepers (15.70 ± 1.12 years of age; training experience 8.35 ± 2.68 years). Main findings: 1. The elite goalkeepers (Group A) focused most of their attention on only one main object (the foot of the opponent’s kicking leg) compared to novice youth goalkeepers (Group B), whose area of interest consisted of more elements: the knee, the lower leg, the foot of the attacking leg, and the ball. 2. The elite goalkeepers (Group A) showed a significantly shorter decision-making time (240–260 ms) than the novice goalkeepers (290–310 ms) in a two-on-one match situation. 3. The use of anticipatory perceptual skills resulted in more accurate anticipation and decision making in elite goalkeepers than in novice goalkeepers, whose perceptual patterns were more dispersed. 4. The anticipatory activity and structure of bioelectric tensions of the rectus femoris (RF) muscle provide useful information for the development of successful anticipatory actions. The ability to recognize signals is a prerequisite for combining movement sequences according to a predetermined pattern and allows for accurate decision making in the goalkeeper’s playing strategy. Full article

Surface electromyographic (sEMG) signal-driven joint-angle estimation plays a critical role in intelligent rehabilitation systems, as its accuracy directly affects both control performance and rehabilitation efficacy. This study proposes a continuous elbow joint angle estimation method based on time–frequency domain analysis. Raw sEMG signals were processed using the Short-Time Fourier Transform (STFT) to extract time–frequency features. A Scale Temporal–Channel Cross-Encoder (STCCE) network was developed, integrating temporal and channel attention mechanisms to enhance feature representation and establish the mapping from sEMG signals to elbow joint angles. The model was trained and evaluated on a dataset comprising approximately 103,000 samples collected from seven subjects. In the single-subject test set, the proposed STCCE model achieved an average Mean Absolute Error (MAE) of $2.96\pm {0.24}^{\circ }$, Root Mean Square Error (RMSE) of $4.41\pm {0.45}^{\circ }$, Coefficient of Determination ($R^2$) of $0.9924\pm 0.0020$, and Correlation Coefficient (CC) of $0.9963\pm 0.0010$. It achieved a MAE of ${3.30}^{\circ }$, RMSE of ${4.75}^{\circ }$, $R^2$ of 0.9915, and CC of 0.9962 on the multi-subject test set, and an average MAE of $15.53\pm {1.80}^{\circ }$, RMSE of $21.72\pm {2.85}^{\circ }$, $R^2$ of $0.8141\pm 0.0540$, and CC of $0.9100\pm 0.0306$ on the inter-subject test set. These results demonstrated that the STCCE model enabled accurate joint-angle estimation in the time–frequency domain, contributing to a better motion intent perception for upper-limb rehabilitation. Full article

Objective: This study aims to investigate the effect of shot selection on the muscle coordination characteristics during basketball shooting. Methods: A three-dimensional motion capture system, force platform, and wireless surface electromyography (sEMG) were used to simultaneously collect shooting data from 14 elite basketball players. An inverse mapping model of sEMG signals and spinal α-motor neuron pool activity was developed based on the Debra muscle segment distribution theory. Non-negative matrix factorization (NMF) and K-means clustering were used to extract muscle coordination features. Results: (1) Significant differences in spinal segment activation timing and amplitude were observed between stationary and jump shots at different distances. In close-range stationary shots, the C5-S3 segments showed higher activation during the TP phase and lower activation during the RP phase. For mid-range shots, the C6-S3 segments exhibited greater activation during the TP phase. In long-range shots, the C7-S3 segments showed higher activation during the TP phase, whereas the L3-S3 segments showed lower activation during the RP phase (p < 0.01). (2) The spatiotemporal structure of muscle coordination modules differed significantly between stationary and jump shots. In terms of spatiotemporal structure, the second and third coordination groups showed stronger activation during the RP phase (p < 0.01). Significant differences in muscle activation levels were also observed between the coordination modules within each group in the spatial structure. Conclusion: Shot selection plays a significant role in shaping neuromuscular control strategies during basketball shooting. Targeted training should focus on addressing the athlete’s specific shooting weaknesses. For stationary shots, the emphasis should be on enhancing lower limb stability, while for jump shots, attention should be directed toward improving core stability and upper limb coordination. Full article

Robot learning from human demonstration pioneers an effective mapping paradigm for endowing robots with human-like operational capabilities. This paper proposes a bio-signal-guided robot adaptive stiffness learning framework grounded in the conclusion that muscle activation of the human arm is positively correlated with the endpoint stiffness. First, we propose a human-teleoperated demonstration platform enabling real-time modulation of robot end-effector stiffness by human tutors during operational tasks. Second, we develop a dual-stage probabilistic modeling architecture employing the Gaussian mixture model and Gaussian mixture regression to model the temporal–motion correlation and the motion–sEMG relationship, successively. Third, a real-world experiment was conducted to validate the effectiveness of the proposed skill transfer framework, demonstrating that the robot achieves online adaptation of Cartesian impedance characteristics in contact-rich tasks. This paper provides a simple and intuitive way to plan the Cartesian impedance parameters, transcending the classical method that requires complex human arm endpoint stiffness identification before human demonstration or compensation for the difference in human–robot operational effects after human demonstration. Full article

The scissor jump (SKJ) is vital in badminton, particularly for backcourt shots, but fatigue increases lower limb load and injury risk. This study investigates how quadriceps fatigue affects biomechanical characteristics and load during SKJ landing, aiming to understand its impact on injury risk. This study involved 27 amateur male badminton players from Ningbo University. Quadriceps fatigue was induced via knee exercises and footwork drills. Biomechanical data before (prior fatigue—PRF) and after fatigue (post fatigue—POF) were recorded using a force platform and motion capture system. Muscle activation was measured with EMG and analyzed through musculoskeletal modeling, with paired t-tests and SPM 1D (Statistical Parametric Mapping 1D) for statistical analysis. Under the POF condition, knee flexion angle increased, and power decreased (p < 0.001, p < 0.001, respectively); ankle plantarflexion angle increased, and power decreased (p < 0.001, p < 0.001, respectively). As fatigue progressed, joint reaction forces initially decreased but later increased. Joint energy dissipation decreased, with differences more pronounced in the coronal than sagittal plane. Achilles tendon force and anterior–posterior tibial shear force decreased, while coronal plane center-of-mass displacement increased. Findings show quadriceps fatigue harms limb stability, upping knee and ankle loads, disrupting the movement pattern, and risking coronal plane injuries. It is recommended that athletes enhance quadriceps endurance, improve neuromuscular control, and refine landing techniques to maintain stability and prevent injuries when fatigued. Full article

This study investigates the use of surface electromyography (sEMG) sensors in measuring muscle activity and mapping it onto wearable textile stretch sensors using a basic deep learning model, the Multi-Layer Perceptron (MLP). Wearable sensors are gaining attention for their ability to monitor physiological data while maintaining user comfort. A three-stage experimental approach was employed to evaluate the mapping process. In the first stage, the impact of applying a low-pass finite impulse response (FIR) filter was assessed by comparing filtered and unfiltered sEMG data. The results showed minimal impact on accuracy (R-squared ~ 0.77), as RMS preprocessing effectively reduced noise. In the second stage, adding tensile velocity data improved the model’s predictive performance (R-squared ~ 0.80), emphasizing the importance of integrating dynamic variables. In the third stage, data from multiple muscle groups, including the biceps brachii, forearm muscles, and triceps brachii, were incorporated, achieving the highest R-squared value of ~0.94. These findings establish wearable textile stretch sensors as reliable tools for monitoring muscle activity during exercise. By demonstrating improved accuracy with a basic MLP model, this study provides a foundation for advancing wearable health monitoring systems and exploring additional physiological parameters and activities. Full article

Instrumented gait analysis provides objective data for clinical assessment, with surface electromyography (EMG) serving as a key tool in identifying abnormal muscle activation. However, reliable reference data considering both age and gender remain limited. Age- and gender-related differences in lower-limb EMG during gait in typically developing individuals were examined in this study using statistical parametric mapping (SPM). We also determined the minimum sample size required for robust clinical reference data. Our findings revealed significant differences in muscle activation patterns across age and gender. Children exhibited increased rectus femoris activation in initial swing and greater hamstring activation in the midstance, whereas adults demonstrated greater semimembranosus activity at initial contact, increased soleus activation at push-off, and greater rectus femoris activity in late swing. Gender-based differences included greater tibialis anterior activation in females during the terminal stance and increased vastus lateralis activity during swing, whereas males showed greater vastus lateralis and biceps femoris activation during terminal swing. Additionally, significant age–gender interaction effects were observed in the biceps femoris and semimembranosus, with gender-related differences becoming more pronounced in adulthood. Power analysis indicates that at least 47 participants, with a minimum of 12 per subgroup (male children, female children, male adults, and female adults), are required to detect age–gender interactions reliably. We strongly recommend incorporating both age and gender in clinical norm bands to enhance the accuracy of gait assessments and improve clinical and research comparisons. Full article

The study of muscle activation patterns using surface electromyography (sEMG) provides critical insights into muscle coordination, enabling advancements in prosthetics, robotics, and rehabilitation by improving intuitive control, replicating human movements, and developing targeted therapeutic strategies. The study involved 15 healthy participants aged 20–27, using Trigno Avanti sensors to record sEMG signals from forearm muscles during specific gestures, with data processed into activation maps to analyze muscle activity and coordination for applications in rehabilitation and prosthetics. The results revealed distinct muscle activation patterns for each gesture, highlighting precise muscle coordination, with specific muscles like m. flexor carpi ulnaris and m. extensor digitorum showing varying levels of involvement depending on the movement, while m. brachioradialis remained inactive across all gestures. The study’s findings enhance our understanding of motor control by revealing specific muscle activation patterns for different hand gestures, highlighting the selectivity of muscle coordination, and suggesting avenues for future research to improve prosthetic design and rehabilitation strategies. Full article

This study evaluates the mapping accuracy between textile stretch sensor data and surface electromyography (sEMG) signals using Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Residual Network (ResNet) models. Data from the forearm, biceps brachii, and triceps brachii were analyzed using Root Mean Square Error (RMSE) and R² as performance metrics. ResNet achieved the lowest RMSE (e.g., 0.1285 for biceps brachii) and highest R² (0.8372), outperforming CNN (RMSE: 0.1455; R²: 0.7639) and MLP (RMSE: 0.1789; R²: 0.6722). The residual learning framework of ResNet effectively handles nonlinear patterns and noise, enabling more accurate predictions even for low-variability datasets like the triceps brachii. CNN showed moderate improvement over MLP by learning temporal features but struggled with low-variability datasets. MLP, as the baseline model, demonstrated the highest RMSE and lowest R², highlighting its limitations in capturing complex relationships. These results suggest the potential reliability of ResNet for mapping textile stretch sensor data to sEMG signals, showing promising performance within the scope of this study. Future research could explore broader applications across different sensor configurations and activities to further validate these findings. Full article

Supernumerary Robotic Limbs (SRLs) are designed to collaborate with the wearer, enhancing operational capabilities. When human limbs are occupied with primary tasks, controlling SRLs flexibly and naturally becomes a challenge. Existing methods such as electromyography (EMG) control and redundant limb control partially address SRL control issues. However, they still face limitations like restricted degrees of freedom and complex data requirements, which hinder their applicability in real-world scenarios. Additionally, fully autonomous control methods, while efficient, often lack the flexibility needed for complex tasks, as they do not allow for real-time user adjustments. In contrast, shared control combines machine autonomy with human input, enabling finer control and more intuitive task completion. Building on our previous work with the mouth-and-tongue interface, this paper integrates a mixed reality (MR) device to form an interactive system that enables shared control of the SRL. The system allows users to dynamically switch between voluntary and autonomous control, providing both flexibility and efficiency. A random forest model classifies 14 distinct tongue and mouth operations, mapping them to six-degree-of-freedom SRL control. In comparative experiments involving ten healthy subjects performing assembly tasks under three control modes (shared control, autonomous control, and voluntary control), shared control demonstrates a balance between machine autonomy and human input. While autonomous control offers higher task efficiency, shared control achieves greater task success rates and improves user experience by combining the advantages of both autonomous operation and voluntary control. This study validates the feasibility of shared control and highlights its advantages in providing flexible switching between autonomy and user intervention, offering new insights into SRL control. Full article