Search Results (25)

Climbing demands exceptional isometric finger flexor strength and neuromuscular efficiency. This study aimed to compare maximum isometric strength and muscle quality (MQ) between climbers and non-climbers and examine the influence of sex and specific grip types. Methods: 33 climbers (14 women) and 29 non-climbers (15 women) volunteered in this study. Maximum isometric strength was measured for handgrip, three-finger drag, and half-crimp grips, while forearm muscle mass was estimated using DXA. MQ was calculated as the ratio of peak isometric force to forearm muscle mass. Results: Climbers demonstrated significantly higher isometric strength in both the three-finger drag and half-crimp grips compared to non-climbers (p < 0.01); however, non-significant differences were observed in handgrip strength. Despite similar forearm muscle mass, climbers exhibited greater MQ. Notably, female non-climbers showed higher MQ than their male counterparts (p < 0.05), a sex difference that was not evident among climbers. All tests exhibited high repeatability (ICC > 0.93, CV < 5.81%) with low SEM and MDC95 values. Conclusions: The findings underscore the necessity of employing climbing-specific strength assessments to capture the unique neuromuscular adaptations induced by climbing training. Muscle quality emerges as a sex-neutral biomarker for strength performance evaluation, with potential applications in the optimization of training programs. Future research should further explore the predictive value of MQ and strive for standardized testing protocols. Full article

Precision and non-damaging harvesting is a key direction for the development of mechanized fruit harvesting technologies. Blueberries, with their soft texture and delicate skin, present significant challenges for achieving precise and non-damaging mechanical harvesting. This paper proposes an intelligent recognition and prediction method based on machine vision. The method uses image recognition technology to extract the physical characteristics of blueberries, such as diameter and thickness, and estimates fruit hardness in real-time through a predictive model. The gripping force of the mechanical claw is dynamically adjusted to ensure non-destructive harvesting. Firstly, a chimpanzee optimization algorithm (ChOA) was used to optimize a prediction model that established a mapping relationship between fruit diameter, thickness, weight, and fruit hardness. The radial basis network optimized by the chimpanzee optimization algorithm (ChOA-RBF) model was compared with a non-optimized model, and the results showed that the ChOA-RBF prediction model has significant advantages in predicting fruit hardness. Next, an orthogonal experiment further verified the model, showing that the prediction error between the model’s values and actual values was less than 5%. Additionally, considering practical applications, a simple and efficient two-parameter method was proposed, removing the weight parameter and predicting fruit hardness using only diameter and thickness. Although the two-parameter method increases the prediction error by 0.36% compared to the three-parameter method, it reduces the number of convergence steps by 71 and shortens the computation time by one-third, significantly improving iteration speed. Finally, further crushing experiments showed that using the two-parameter method for hardness prediction through parameter extraction via visual recognition resulted in a relative error of less than 8%, with an average relative error of 3.91%. The error falls within the acceptable range for the safety factor design. This method provides a novel solution for the non-damaging mechanized harvesting of soft fruits. Full article

Background: Prostheses are becoming more advanced and biomimetic with time, providing additional capabilities to their users. However, prosthetic sensation lags far behind its natural limb counterpart, limiting the use of sensory feedback in prosthetic motion planning and execution. Without actionable sensation, prostheses may never meet the functional requirements to match biological performance. Methods: We propose an approach for upper limb prosthetic grasp security feedback, delivered to the wearer through direct nerve stimulation proportional to the likelihood of objects slipping from grasp. This proportional feedback is based on a linear regression of the sensors embedded in a prosthetic hand to predict slip before it occurs. Four participants with transhumeral amputation performed pulling tasks with their prosthetic hand grasping an object at predetermined grip forces, attempting to pull the object with as much force as possible without slip. These trials were performed with two different prediction notification paradigms. Results: At lower grasp forces, where slip was more likely, a strong, single impulse notification of impending slip reduced the incidence of object slip by a median of 32%, but the maximum achieved pull forces did not change. At higher grasp forces, where slip was less likely, the maximum achieved pull forces increased by a median of 19% across participants when provided with a stimulation strength inversely proportional to the grasp security, but slip incidence was unchanged. Conclusions: These results suggest that this approach may be effective in recreating a lost sense of grip stability in the missing limb that can be incorporated into motor planning and ultimately prevent unanticipated object slips. Full article

This study aimed to predict and fit the nonlinear dynamic grip force of the human upper limb using surface electromyographic (sEMG) signals. The research employed a time-series-based neural network, NARX, to establish a mapping relationship between the electromyographic signals of the forearm muscle groups and dynamic grip force. Three-channel electromyographic signal acquisition equipment and a grip force sensor were used to record muscle signals and grip force data of the subjects under specific dynamic force conditions. After preprocessing the data, including outlier removal, wavelet denoising, and baseline drift correction, the NARX model was used for fitting analysis. The model compares two different training strategies: regularized stochastic gradient descent (BRSGD) and conjugate gradient (CG). The results show that the CG greatly shortened the training time, and performance did not decline. NARX demonstrated good accuracy and stability in dynamic grip force prediction, with the model with 10 layers and 20 time delays performing the best. The results demonstrate that the proposed method has potential practical significance for force control applications in smart prosthetics and virtual reality. Full article

Background/Objectives: The potential application of electromyography (EMG) as a method for precise force control in prosthetic devices is investigated, expanding on its traditional use in gesture detection. Variability in EMG signals among individuals is influenced by physiological factors such as muscle mass, body fat percentage, and subcutaneous fat, as well as demographic variables like age, gender, height, and weight. This study aims to evaluate how these factors impact EMG signal quality and force output. Methods: EMG data was normalized using the maximum voluntary contraction (MVC) method, recorded at 100%, 50%, and 25% of MVC with simultaneous grip force measurement. Physiological parameters, including fat percentage, subcutaneous fat, and muscle mass, were analyzed. An extreme gradient boosting algorithm was applied to model the relationship between EMG amplitude and grip force. Results: The findings demonstrated significant linear correlations, with r² coefficients reaching up to 0.93 and 0.83 in most cases. Muscle mass and fat levels were identified as key determinants of EMG variability, with significance coefficients ranging from 0.36592 to 0.0856 for muscle mass and 0.281918 to 0.06001 for fat levels. Conclusions: These results underscore the potential of EMG to enhance force control in prosthetic limbs, particularly in tasks such as grasping, holding, and releasing objects. Incorporating body composition factors into EMG-based prediction algorithms offers a refined approach to improving the precision and functionality of prosthetic control systems for complex motor tasks. Full article

Background: Sleep disorders have been poorly described in congenital (CDM) and childhood (ChDM) myotonic dystrophy despite being highly burdensome. The aims of this study were to explore sleep disorders in a cohort of Italian CDM and ChDM and to assess their association with motor and respiratory function and disease-specific cognitive and behavioral assessments. Methods: This was an observational multicenter study. Reported sleep quality was assessed using the Pediatric Daytime Sleepiness Scale (PDSS) and Pediatric Sleep Questionnaire (PSQ). Sleep quality was correlated to motor function (6 min walk test, 6MWT and grip strength; pulmonary function (predicted Forced Vital Capacity%, FVC% pred.); executive function assessed by BRIEF-2; autism traits assessed by Autism Spectrum Screening Questionnaire (ASSQ) and Repetitive Behavior Scale-revised (RBS-R); Quality of life (PedsQL) and disease burden (Congenital Childhood Myotonic Dystrophy Health Index, CCMDHI). Results: Forty-six patients were included, 33 CDM and 13 ChDM, at a median age of 10.4 and 15.1 years. Daytime sleepiness and disrupted sleep were reported by 30% children, in both subgroups of CDM and ChDM. Daytime sleepiness correlated with autism traits in CDM (p < 0.05). Disrupted sleep correlated with poorer executive function (p = 0.04) and higher disease burden (p = 0.03). Conclusions: Sleep issues are a feature of both CDM and ChDM. They correlate with behavioral issues and impact on disease burden. Full article

Tomatoes are prone to mechanical damage due to improper gripping forces during automated harvest and postharvest processes. To reduce this damage, a dynamic viscoelastic model based on long short-term memory (LSTM) is proposed to fit the dynamic compression stress relaxation characteristics of the individual fruit. Furthermore, the classical stress relaxation models involved, the triple-element Maxwell and Caputo fractional derivative models, are compared with the LSTM model to validate its performance. Meanwhile, the LSTM and classical stress relaxation models are used to predict the stress relaxation characteristics of tomato fruit with different fruit sizes and compression positions. The results for the whole test dataset show that the LSTM model achieves a RMSE of $2.829\times {10}^{-5}$ Mpa and a MAPE of 0.228%. It significantly outperforms the Caputo fractional derivative model by demonstrating a substantial enhancement with a 37% decrease in RMSE and a 36% reduction in MAPE. Further analysis of individual tomato fruit reveals the LSTM model’s performance, with the minimum RMSE recorded at the septum position being $3.438\times {10}^{-5}$ Mpa, 31% higher than the maximum RMSE at the locule position. Similarly, the lowest MAPE at the septum stands at 0.375%, outperforming the highest MAPE at the locule position by a significant margin of 90%. Moreover, the LSTM model consistently reports the smallest discrepancies between the predicted and observed values compared to classical stress relaxation models. This accuracy suggests that the LSTM model could effectively supplant classical stress relaxation models for predicting stress relaxation changes in individual tomato fruit. Full article

Muscles play an indispensable role in human life. Surface electromyography (sEMG), as a non-invasive method, is crucial for monitoring muscle status. It is characterized by its real-time, portable nature and is extensively utilized in sports and rehabilitation sciences. This study proposed a wireless acquisition system based on multi-channel sEMG for objective monitoring of grip force. The system consists of an sEMG acquisition module containing four-channel discrete terminals and a host computer receiver module, using Bluetooth wireless transmission. The system is portable, wearable, low-cost, and easy to operate. Leveraging the system, an experiment for grip force prediction was designed, employing the bald eagle search (BES) algorithm to enhance the Random Forest (RF) algorithm. This approach established a grip force prediction model based on dual-channel sEMG signals. As tested, the performance of acquisition terminal proceeded as follows: the gain was up to 1125 times, and the common mode rejection ratio (CMRR) remained high in the sEMG signal band range (96.94 dB (100 Hz), 84.12 dB (500 Hz)), while the performance of the grip force prediction algorithm had an R² of 0.9215, an MAE of 1.0637, and an MSE of 1.7479. The proposed system demonstrates excellent performance in real-time signal acquisition and grip force prediction, proving to be an effective muscle status monitoring tool for rehabilitation, training, disease condition surveillance and scientific fitness applications. Full article

As manufacturing and assembly processes continue to require more adaptable systems for automated handling, innovative solutions for universal gripping are emerging. These grasping systems can enable the handling of wide varieties of shapes, with gripping forces varying with grasped geometries. For the efficient usage of handling systems, precise offline and online prediction models for resulting grasping forces for different objects are necessary. In previous research, a flexible vacuum-based granular gripper was developed, for which no option for predicting gripping forces is currently available. Various gripping force prediction methodologies within the current state of the art are examined and evaluated. For an assessment of grasping forces of previously untested objects for the examined gripper with limited data and low computational effort, two methodologies are proposed. An analytical, 2D-geometry-derived gripper-specific metric for geometries is compared to a methodology based on similarities of objects to a small existing dataset. The applicability and prediction quality for different object types is analyzed through validation experiments. Gripping force estimations are possible with both methodologies, with individual weaknesses towards geometric features such as air permeabilities. With further development, robust predictions of gripping forces could be achieved for a wide range of unknown object geometries with limited experimental effort. Full article

Dielectric elastomer actuators (DEAs) offer robust, high-energy-density solutions for soft robotics. The proposed end effector consists of three spring roll configuration DEAs, each acting as a robotic finger, using a 3M VHB-F9473PC adhesive membrane. Spring roll DEAs can be designed to achieve highly specialised actuations depending on the electrode patterning and structural supports. This allows a spring roll DEA-based soft end effector to be tailor-made by simply altering the electrode patterning. The lateral force, bending angle and response time of the actuator are measured experimentally and compared with the predictions of an analytical model. The cylindrical actuator measures 70 mm in length and 15 mm in diameter and achieves a lateral force of 30 mN, a bending angle of 6.8° and a response time of $\approx$1 s. Spring roll configuration DEAs are shown to reduce the effects of viscoelasticity seen in the membrane, making the actuator more controllable at higher voltages. The dielectric constant of the membrane is shown to be a limiting factor of actuation, with a decrease in dielectric constant resulting in larger actuation. The end effector successfully grips numerous light objects for extended periods, showing the applicability of spring roll DEAs for soft end effectors. Full article

(1) Background: Observational studies associate vitamin D deficiency with muscle disorders, while some clinical trial data support a minor association between the vitamin and skeletal muscle performance in healthy subjects. Vitamin D receptor knockout mice studies confirm the relationship between vitamin D and skeletal muscle; however, causal inference in humans is challenging due to the ethical implications of including vitamin D-deficient participants in randomized trials. This study uses genetic methods to safely explore causal underpinnings for the relationship between 25(OH)D concentrations and skeletal muscle-related traits, including grip strength and combined arm skeletal muscle mass, and extends this analysis to suspected pathophysiology in the form of probable sarcopenia and sarcopenic obesity. (2) Methods: We conducted Mendelian randomization (MR) analyses in up to 307,281 participants from the UK Biobank of whom 25,414 had probable sarcopenia and 16,520 had sarcopenic obesity. In total, 35 variants were used to instrument 25(OH)D and MR analyses conducted using multiple approaches. (3) Results: Genetic analyses provided support for a relationship between genetically predicted higher 25(OH)D and skeletal muscle traits, with linear MR analyses for grip strength showing 0.11 kg (95% CI 0.04, 0.19) greater contractile force per 10 unit higher 25(OH)D, while there was a modest association with skeletal muscle mass (0.01 kg (95% CI 0.003, 0.02) greater muscle mass). For probable sarcopenia risk, there was suggestive evidence for lower odds by higher 25(OH)D (OR 0.96 (95% CI 0.92, 1.00)); however, this did not reflect an association with sarcopenic obesity (OR 0.97 (95% CI 0.93, 1.02)), but was seen in probable sarcopenia cases who were not obese (OR 0.92 (95% CI 0.86, 0.98)). Results were similar across multiple MR approaches. (4) Conclusions: Our study supports a causal relationship between 25(OH)D and skeletal muscle health. While evidence for benefit did not extend to lower risk of sarcopenic obesity, effective vitamin D-deficiency prevention strategies may help reduce age-related muscle weakness. Full article

This manuscript investigates the rotational mechanical impedance of the human hand–arm system with respect to vibration excitation around the gripping axis of the hand under the influence of body posture, gripping force, and push force. Knowledge of rotational mechanical impedance is required for deriving models of hand–arm biodynamics. These models are used in the validation of power tools to predict further vibrational human–machine interactions. In the current state of research, such models exist for translational but not rotational vibration excitation. Consequently, this study investigates the properties of a hand–arm system with respect to rotational vibration excitation. In the study, the rotational impedance of the hand–arm systems of 13 adults was measured at various gripping and push forces applied in different body postures. The setup of the test used in this study consisted of a shaker that applied rotational vibrations at certain frequencies to the subjects’ hand–arm systems via a cylindrical handle. The results of the study indicate a spring–damper dynamic of the hand–arm system. The gripping force strongly influences the magnitude of rotational impedance across the frequency spectrum. Regarding push force and posture, no corresponding influence could be determined. The results suggest that the frictional contact between the hand and handle might confer a damping effect. Full article

Haptics plays a significant role not only in the rehabilitation of neurological disorders, such as stroke, by substituting necessary cognitive information but also in human–computer interfaces (HCIs), which are now an integral part of the recently launched metaverse. This study proposes a unique, soft, monolithic haptic feedback device (SoHapS) that was directly manufactured using a low-cost and open-source fused deposition modeling (FDM) 3D printer by employing a combination of soft conductive and nonconductive thermoplastic polyurethane (TPU) materials (NinjaTek, USA). SoHapS consists of a soft bellow actuator and a soft resistive force sensor, which are optimized using finite element modeling (FEM). SoHapS was characterized both mechanically and electrically to assess its performance, and a dynamic model was developed to predict its force output with given pressure inputs. We demonstrated the efficacy of SoHapS in substituting biofeedback with tactile feedback, such as gripping force, and proprioceptive feedback, such as finger flexion–extension positions, in the context of teleoperation. With its intrinsic properties, SoHapS can be integrated into rehabilitation robots and robotic prostheses, as well as augmented, virtual, and mixed reality (AR/VR/MR) systems, to induce various types of bio-mimicked feedback. Full article

Grip strength (GS) is correlated with major muscle group strength; weakness and asymmetry in older adults are predictive of future disease and functional limitation risk. GS at different ages and hand symmetry for Olympic-style weightlifters and their association with performance have not been established. GS was measured in 164 athletes participating in the 2022 World Master Weightlifting Championships. The objectives wereto study the magnitude of the age-associated decline in GS in weightlifters and the association of GS with weightlifting performance. Hand symmetry was considered as a potential factor in successful lifts. Ages ranged from 35 to 90 (mean 53 years). Participants reported weekly training averages of 8.3 h of weightlifting and 4.1 additional hours of physical activities. The age-associated decline in GS was less steep than the decline in weightlifting performance. GS was lower in weightlifters compared to athletes in other sports that require grasping or force application (t = −2.53, p=0.053 for females; t = −2.62, p= 0.029 for males). The rate of decline was similar across different populations (weightlifters, other athletes, community-dwelling adults). Height and age were associated with GS, but performance level and training hours were not. GS was associated with snatch performance (t = 3.56, p < 0.001) but not with clean and jerk (t = 0.48, p = 0.633). Full article

New technologies for monitoring grip forces during hand and finger movements in non-standard task contexts have provided unprecedented functional insights into somatosensory cognition. Somatosensory cognition is the basis of our ability to manipulate and transform objects of the physical world and to grasp them with the right amount of force. In previous work, the wireless tracking of grip-force signals recorded from biosensors in the palm of the human hand has permitted us to unravel some of the functional synergies that underlie perceptual and motor learning under conditions of non-standard and essentially unreliable sensory input. This paper builds on this previous work and discusses further, functionally motivated, analyses of individual grip-force data in manual robot control. Grip forces were recorded from various loci in the dominant and non-dominant hands of individuals with wearable wireless sensor technology. Statistical analyses bring to the fore skill-specific temporal variations in thousands of grip forces of a complete novice and a highly proficient expert in manual robot control. A brain-inspired neural network model that uses the output metric of a self-organizing pap with unsupervised winner-take-all learning was run on the sensor output from both hands of each user. The neural network metric expresses the difference between an input representation and its model representation at any given moment in time and reliably captures the differences between novice and expert performance in terms of grip-force variability.Functionally motivated spatiotemporal analysis of individual average grip forces, computed for time windows of constant size in the output of a restricted amount of task-relevant sensors in the dominant (preferred) hand, reveal finger-specific synergies reflecting robotic task skill. The analyses lead the way towards grip-force monitoring in real time. This will permit tracking task skill evolution in trainees, or identify individual proficiency levels in human robot-interaction, which represents unprecedented challenges for perceptual and motor adaptation in environmental contexts of high sensory uncertainty. Cross-disciplinary insights from systems neuroscience and cognitive behavioral science, and the predictive modeling of operator skills using parsimonious Artificial Intelligence (AI), will contribute towards improving the outcome of new types of surgery, in particular the single-port approaches such as NOTES (Natural Orifice Transluminal Endoscopic Surgery) and SILS (Single-Incision Laparoscopic Surgery). Full article