Estimate Muscle Forces Research Articles

The cholesterol depleting agent, (2-Hydroxypropyl)-ß-cyclodextrin, does not affect disease progression in SOD1G93A mice

Objective Previously, we demonstrated that Amyloid Precursor Protein (APP) contributes to pathology in the SOD1G93A mouse model of ALS and that genetic ablation of APP in SOD1G93A mice significantly improved multiple disease parameters, including muscle innervation and motor neuron survival. We also observed elevated levels of potentially neurotoxic Aß peptides that have been implicated in Alzheimer’s Disease (AD) pathogenesis, within motor neurons and astrocytes in SOD1G93A mice. More recently, it has been shown that blocking Aß production improves outcome measures in SOD1G93A mice. The cyclodextrin, (2-Hydroxypropyl)-ß-cyclodextrin (HP-β-CD), has previously been shown to deplete intraneuronal unesterified cholesterol, resulting in effective reduction of Aß production and amelioration of disease progression in mouse models of AD and Niemann Pick Type C (NPC) disease. Here, we tested whether HP-β-CD could also improve phenotypic progression in SOD1G93A mice. Methods Pre-symptomatic male SOD1G93A mice were randomly assigned to the following treatment groups: HP-β-CD (4000mg/kg, n = 9) or vehicle (saline; n = 10), delivered by weekly subcutaneous injection, commencing at 67 days of age. Longitudinal grip-strength and body mass analysis was performed until late-stage disease (120 days of age), followed by in vivo bilateral isometric muscle tension analysis of tibialis anterior (TA) and extensor digitorum longus (EDL) muscles. Results: HP-β-CD administration had no effect on body mass or grip-strength compared to vehicle treated SOD1G93A mice. Similarly, HP-β-CD treatment had no effect on muscle force, contractile properties or motor unit number estimates (MUNE) at late-stage disease in SOD1G93A mice. Conclusion This study shows that HP-β-CD does not confer any therapeutic benefit in SOD1G93A mice. However, the absence of detrimental effects is informative, given the common use of cyclodextrins as complexing agents for other pharmaceutical products, their standalone therapeutic potential and the emerging association between dyslipidaemia and ALS progression.

Muscle force estimation during gait using Angle-EMG-Force relationship.

Measuring the muscle force during gait can provide crucial knowledge for clarifying the walking mechanism and preventing injuries. However, non-invasive muscle force measurement is a major challenge in biomechanics. Previous research has investigated the relationship between the amplitude of electromyography (EMG) and muscle force. By examining the EMG-force relationship of each muscle, the generated muscle force can be measured on the basis of the EMG amplitude during gait. This study aimed to investigate the angle-EMG-force relationship of lower limb muscles and estimate the muscle force during gait. The EMG and muscle force were measured in a static muscle force measurement task, and the angle-EMG-force relationship was analyzed based on these data. The results indicate that the muscle force can be estimated using the angle-EMG-force relationship during gait.Clinical Relevance-This study contributes to a more correct analysis of the muscle force during gait.

A new force profile signal for a convex solution of muscle force estimation from electromyographic signals.

High-Density Surface Electromyography (HD-sEMG) is a non-invasive technique for measuring the electrical activity of a muscle with multiple, closely spaced electrodes. Estimation of muscle force is one of the applications of HD-sEMG. Usually, validating different EMG-Force models entails simple movements limited to laboratory settings. The validity of these models in more ecological conditions, requesting force production over a wide frequency band, remains unknown. In this study, we, therefore, compare the results of force prediction using four different types of input force profiles that can be representative of daily life activities, and we investigate whether the crest factor of these different input signals affects force prediction. For predicting the force from sEMG signals, we used our real-time and convex methods. HD-sEMG signals were recorded with 144 channels from the biceps brachii, brachioradialis, and triceps (long, lateral, and medial head) muscles of 24 healthy subjects during random signal, random phase, Schroeder phase, and minimum crest factor (crestmin) signal. The correlation and coefficient of determination (R2) between measured and predicted forces were calculated for the different force feedback profiles. The crestmin signal showed significantly better results based on statistical tests (P-value < 0.05), with correlation and R2 equal to 0.92±0.03 and 0.86±0.05, respectively. The results demonstrate that the crest factor of input signals is a crucial parameter that can impact the performance of EMG-Force models and must be considered during training.Clinical Relevance- This study demonstrates that lower crest factor multisine force profiles result in improved fitness for force prediction and can be used as an alternative to random signals.

Model-based estimation of muscle and ACL forces during turning maneuvers in alpine skiing

In alpine skiing, estimation of the muscle forces and joint loads such as the forces in the ACL of the knee are essential to quantify the loading pattern of the skier during turning maneuvers. Since direct measurement of these forces is generally not feasible, non-invasive methods based on musculoskeletal modeling should be considered. In alpine skiing, however, muscle forces and ACL forces have not been analyzed during turning maneuvers due to the lack of three dimensional musculoskeletal models. In the present study, a three dimensional musculoskeletal skier model was successfully applied to track experimental data of a professional skier. During the turning maneuver, the primary activated muscles groups of the outside leg, bearing the highest loads, were the gluteus maximus, vastus lateralis as well as the medial and lateral hamstrings. The main function of these muscles was to generate the required hip extension and knee extension moments. The gluteus maximus was also the main contributor to the hip abduction moment when the hip was highly flexed. Furthermore, the lateral hamstrings and gluteus maximus contributed to the hip external rotation moment in addition to the quadratus femoris. Peak ACL forces reached 211 N on the outside leg with the main contribution in the frontal plane due to an external knee abduction moment. Sagittal plane contributions were low due to consistently high knee flexion (> 60^{circ }), substantial co-activation of the hamstrings and the ground reaction force pushing the anteriorly inclined tibia backwards with respect to the femur. In conclusion, the present musculoskeletal simulation model provides a detailed insight into the loading of a skier during turning maneuvers that might be used to analyze appropriate training loads or injury risk factors such as the speed or turn radius of the skier, changes of the equipment or neuromuscular control parameters.

Determinants of joint stiffness and jumping height during drop jump.

The present study aimed to examine the effects of muscle-tendon mechanical properties and electromyographic activity on joint stiffness and jumping height and to explore the determinants of joint stiffness and jumping height. Twenty-nine males performed unilateral drop jumps at three drop heights (10, 20, and 30 cm) using only the ankle joint on the sledge apparatus. Ankle joint stiffness, jumping height, and electromyographic activity of the plantar flexor muscles were measured during drop jumps. Active muscle stiffness of the medial gastrocnemius muscle was calculated according to changes in the estimated muscle force and fascicle length during fast stretching at five different angular velocities (100, 200, 300, 500, and 600 deg s-1 ) after submaximal isometric contractions. Tendon stiffness and elastic energy were measured during ramp and ballistic contractions. Active muscle stiffness was significantly correlated with joint stiffness, except for a few conditions. Tendon stiffness measured during ramp and ballistic contractions was not significantly correlated with joint stiffness. The ratios of electromyographic activity before landing and during the eccentric phase to that during the concentric phase were significantly correlated with joint stiffness. In addition, jumping heights at 10 and 20 cm (except for 30 cm) drop heights were strongly associated with the tendon elastic energy, whereas no other measured variables showed significant correlations with jumping heights. These results suggested that (1) joint stiffness is determined by active muscle stiffness and electromyographic activity patterns during jumping, and (2) jumping height is determined by tendon elastic energy.

Effect of neglecting passive spinal structures: a quantitative investigation using the forward-dynamics and inverse-dynamics musculoskeletal approach.

Purpose: Inverse-dynamics (ID) analysis is an approach widely used for studying spine biomechanics and the estimation of muscle forces. Despite the increasing structural complexity of spine models, ID analysis results substantially rely on accurate kinematic data that most of the current technologies are not capable to provide. For this reason, the model complexity is drastically reduced by assuming three degrees of freedom spherical joints and generic kinematic coupling constraints. Moreover, the majority of current ID spine models neglect the contribution of passive structures. The aim of this ID analysis study was to determine the impact of modelled passive structures (i.e., ligaments and intervertebral discs) on remaining joint forces and torques that muscles must balance in the functional spinal unit. Methods: For this purpose, an existing generic spine model developed for the use in the demoa software environment was transferred into the musculoskeletal modelling platform OpenSim. The thoracolumbar spine model previously used in forward-dynamics (FD) simulations provided a full kinematic description of a flexion-extension movement. By using the obtained in silico kinematics, ID analysis was performed. The individual contribution of passive elements to the generalised net joint forces and torques was evaluated in a step-wise approach increasing the model complexity by adding individual biological structures of the spine. Results: The implementation of intervertebral discs and ligaments has significantly reduced compressive loading and anterior torque that is attributed to the acting net muscle forces by -200% and -75%, respectively. The ID model kinematics and kinetics were cross-validated against the FD simulation results. Conclusion: This study clearly shows the importance of incorporating passive spinal structures on the accurate computation of remaining joint loads. Furthermore, for the first time, a generic spine model was used and cross-validated in two different musculoskeletal modelling platforms, i.e., demoa and OpenSim, respectively. In future, a comparison of neuromuscular control strategies for spinal movement can be investigated using both approaches.

Kinematic and muscle force asymmetry in healthy runners: How do different methods measure up?

BackgroundComparing the performance of one leg to another is a common means of assessing running gait to help inform clinical management strategies. Various methods are employed to quantify asymmetries between limbs. However, limited data is available describing the amount of asymmetry that may be expected during running and no index has been identified as preferable for making a clinical determination of asymmetry. Therefore, this study aimed to describe amounts of asymmetry in collegiate cross-country runners and compare different methods of calculating asymmetry. Research questionWhat can be expected as a normal amount of asymmetry in biomechanical variables in healthy runners when using different indices to quantify limb symmetry? MethodsSixty-three (29 male and 34 female) runners participated. Running mechanics were assessed during overground running using 3D motion capture and a musculoskeletal model using static optimization to estimate muscle forces. Independent t-tests were utilized to determine statistical differences in variables between legs. Different methods of quantifying asymmetry were then compared to statistical differences between limbs to determine cut-off values and the sensitivity and specificity of each method. ResultsA large portion of runners demonstrated asymmetry during running. Kinematic variables can be expected to have small differences (2–3 degrees) between limbs while muscle forces may show greater amounts of asymmetry. The sensitivities and specificities for each method of calculating asymmetry were similar, however, different methods led to different cut-off values for each variable investigated. SignificanceAsymmetry can be expected between limbs during running. However, when assessing asymmetry, practitioners should consider the joint, variable, and method of calculating asymmetry when determining differences between limbs.

Investigation of a wearable piezoelectric-IMU multi-modal sensing system for real-time muscle force estimation

Piezoelectric based ultrasonic transducer shows a promising application prospect in the wearable muscle force estimator by detecting the morphological-biochemical peculiarity of human motion. However, due to the nonlinearity of muscle contraction, muscle force estimation in a dynamic motion, such as leg lifting, is still a challenge. In this study, a wearable multi-sensory system was developed for muscle force estimation in the isometric contraction assessment and during the dynamic training. A customized wearable ultrasound system was adopted for real-time deformation measurement of muscle, and an inertial measurement unit sensor was utilized to detect the joint angle. Thus, the muscle force can be predicted by identifying the muscle deformation as well as considering the muscle thickness change caused by the joint angel variation. The robustness and efficiency of the system was investigated by evaluating the muscle force of the rectus femoris during the isometric contraction assessment and the knee’s dynamic exercise. The accuracy of muscle force prediction is over 90%. During the knee’s dynamic exercise, the predicted force output of the lower-limb agreed well with the measured value, demonstrating the promising application of the system in dynamic muscle force estimation. This approach can provide real-time muscle force information for the patients to improve the rehabilitative training effect when using an exoskeletal rehabilitation robot as well as evaluate their recovery situation.

Linking muscle architecture and function in vivo: conceptual or methodological limitations?

Despite the clear theoretical link between sarcomere arrangement and force production, the relationship between muscle architecture and function remain ambiguous in vivo. We used two frequently used ultrasound-based approaches to assess the relationships between vastus lateralis architecture parameters obtained in three common conditions of muscle lengths and contractile states, and the mechanical output of the muscle in twenty-one healthy subjects. The relationship between outcomes obtained in different conditions were also examined. Muscle architecture was analysed in panoramic ultrasound scans at rest with the knee fully extended and in regular scans at an angle close to maximum force (60°), at rest and under maximum contraction. Isokinetic and isometric strength tests were used to estimate muscle force production at various fascicle velocities. Measurements of fascicle length, pennation angle and thickness obtained under different experimental conditions correlated moderately with each other (r = 0.40-.74). Fascicle length measured at 60°at rest correlated with force during high-velocity knee extension (r = 0.46 at 400°s-1) and joint work during isokinetic knee extension (r = 0.44 at 200°s-1 and r = 0.57 at 100°s-1). Muscle thickness was related to maximum force for all measurement methods (r = 0.44-0.73). However, we found no significant correlations between fascicle length or pennation angle and any measures of muscle force or work. Most correlations between architecture and force were stronger when architecture was measured at rest close to optimal length. These findings reflect methodological limitations of current approaches to measure fascicle length and pennation angle in vivo. They also highlight the limited value of static architecture measurements when reported in isolation or without direct experimental context.

A real-time and convex model for the estimation of muscle force from surface electromyographic signals in the upper and lower limbs.

Surface electromyography (sEMG) is a signal consisting of different motor unit action potential trains and records from the surface of the muscles. One of the applications of sEMG is the estimation of muscle force. We proposed a new real-time convex and interpretable model for solving the sEMG-force estimation. We validated it on the upper limb during isometric voluntary flexions-extensions at 30%, 50%, and 70% Maximum Voluntary Contraction in five subjects, and lower limbs during standing tasks in thirty-three volunteers, without a history of neuromuscular disorders. Moreover, the performance of the proposed method was statistically compared with that of the state-of-the-art (13 methods, including linear-in-the-parameter models, Artificial Neural Networks and Supported Vector Machines, and non-linear models). The envelope of the sEMG signals was estimated, and the representative envelope of each muscle was used in our analysis. The convex form of an exponential EMG-force model was derived, and each muscle's coefficient was estimated using the Least Square method. The goodness-of-fit indices, the residual signal analysis (bias and Bland-Altman plot), and the running time analysis were provided. For the entire model, 30% of the data was used for estimation, while the remaining 20% and 50% were used for validation and testing, respectively. The average R-square (%) of the proposed method was 96.77 ± 1.67 [94.38, 98.06] for the test sets of the upper limb and 91.08 ± 6.84 [62.22, 96.62] for the lower-limb dataset (MEAN ± SD [min, max]). The proposed method was not significantly different from the recorded force signal (p-value = 0.610); that was not the case for the other tested models. The proposed method significantly outperformed the other methods (adj. p-value < 0.05). The average running time of each 250ms signal of the training and testing of the proposed method was 25.7 ± 4.0 [22.3, 40.8] and 11.0 ± 2.9 [4.7, 17.8] in microseconds for the entire dataset. The proposed convex model is thus a promising method for estimating the force from the joints of the upper and lower limbs, with applications in load sharing, robotics, rehabilitation, and prosthesis control for the upper and lower limbs.

Age-Related Reliability of B-Mode Analysis for Tailored Exosuit Assistance.

In the field of wearable robotics, assistance needs to be individualized for the user to maximize benefit. Information from muscle fascicles automatically recorded from brightness mode (B-mode) ultrasound has been used to design assistance profiles that are proportional to the estimated muscle force of young individuals. There is also a desire to develop similar strategies for older adults who may have age-altered physiology. This study introduces and validates a ResNet + 2x-LSTM model for extracting fascicle lengths in young and older adults. The labeling was generated in a semimanual manner for young (40,696 frames) and older adults (34,262 frames) depicting B-mode imaging of the medial gastrocnemius. First, the model was trained on young and tested on both young (R2 = 0.85, RMSE = 2.36 ± 1.51 mm, MAPE = 3.6%, aaDF = 0.48 ± 1.1 mm) and older adults (R2 = 0.53, RMSE = 4.7 ± 2.51 mm, MAPE = 5.19%, aaDF = 1.9 ± 1.39 mm). Then, the performances were trained across all ages (R2 = 0.79, RMSE = 3.95 ± 2.51 mm, MAPE = 4.5%, aaDF = 0.67 ± 1.8 mm). Although age-related muscle loss affects the error of the tracking methodology compared to the young population, the absolute percentage error for individual fascicles leads to a small variation of 3-5%, suggesting that the error may be acceptable in the generation of assistive force profiles.

Validation of an EMG submaximal method to calibrate a novel dynamic EMG-driven musculoskeletal model of the trunk: Effects on model estimates.

Multijoint EMG-assisted optimization models are reliable tools to predict muscle forces as they account for inter- and intra-individual variations in activation. However, the conventional method of normalizing EMG signals using maximum voluntary contractions (MVCs) is problematic and introduces major limitations. The sub-maximal voluntary contraction (SVC) approaches have been proposed as a remedy, but their performance against the MVC approach needs further validation particularly during dynamic tasks. To compare model outcomes between MVC and SVC approaches, nineteen healthy subjects performed a dynamic lifting task with two loading conditions. Results demonstrated that these two approaches produced highly correlated results with relatively small absolute and relative differences (<10%) when considering highly-aggregated model outcomes (e.g. compression forces, stability indices). Larger differences were, however, observed in estimated muscle forces. Although some model outcomes, e.g. force of abdominal muscles, were statistically different, their effect sizes remained mostly small (ηG2≤0.13) and in a few cases moderate (ηG2≤0.165). The findings highlight that the MVC calibration approach can reliably be replaced by the SVC approach when the true MVC exertion is not accessible due to pain, kinesiophobia and/or the lack of proper training.

A Transformer-based Multi-task Learning Framework for Myoelectric Pattern Recognition Supporting Muscle Force Estimation.

Simultaneous implementation of myoelectric pattern recognition and muscle force estimation is highly demanded in building natural gestural interfaces but a challenging task due to the gesture classification accuracy degradation under varying muscle strengths. To address this problem, a novel method using transformer-based multi-task learning (MTL-Transformer) for the prediction of both myoelectric patterns and corresponding muscle strengths was proposed to describe the inherent characteristics of an individual gesture pattern under different force conditions, thereby improving the accuracy of myoelectric pattern recognition. In addition, the transformer model enabled the characterization of long-term temporal correlations to ensure precise and smooth estimation of the muscle force. The performance of the proposed MTL-Transformer framework was evaluated via experiments of classifying eleven hand gestures and estimating the corresponding muscle force simultaneously, using high-density surface electromyogram (HD-sEMG) recordings from forearm flexor muscles of eleven intact-limbed subjects. The MTL-Transformer framework yielded high classification accuracy (98.70±1.21%) and low root mean square deviation (12.59±2.76%), and outperformed other two common temporally modelling methods significantly (p < 0.05) in terms of both improved gesture recognition accuracies and reduced muscle force estimation errors. The MTL-Transformer framework is demonstrated as an effective solution for simultaneous implementation of myoelectric pattern recognition and muscle force estimation. This study promotes the development of robust and smooth myoelectric control systems, with wide applications in gestural interfaces, prosthetic and orthotic control.

On the estimation of hip joint loads through musculoskeletal modeling.

Noninvasive estimation of joint loads is still an open challenge in biomechanics. Although musculoskeletal modeling represents a solid resource, multiple improvements are still necessary to obtain accurate predictions of joint loads and to translate such potential into practical utility. The present study, focused on the hip joint, is aimed at reviewing the state-of-the-art literature on the estimation of hip joint reaction forces through musculoskeletal modeling. Our literature inspection, based on well-defined selection criteria, returned seventeen works, which were compared in terms of methods and results. Deviations between predicted and in vivo measured hip joint loads, taken from the OrthoLoad database, were assessed through quantitative deviation indices. Despite the numerous modeling and computational improvements made over the last two decades, predicted hip joint loads still deviate from their experimental counterparts and typically overestimate them. Several critical aspects have emerged that affect muscle force estimation, hence joint loads. Among them, the physical fidelity of the musculoskeletal model, with its parameters and geometry, plays a crucial role. Also, predicted joint loads are markedly affected by the selected muscle recruitment strategy, which reflects the underlying motor control policy. Practical guidelines for researchers interested in noninvasive estimation of hip joint loads are also provided.

Upper Extremity Joint Torque Estimation Through an Electromyography-Driven Model

Abstract Cerebrovascular accidents like a stroke can affect the lower limb as well as upper extremity joints (i.e., shoulder, elbow, or wrist) and hinder the ability to produce necessary torque for activities of daily living. In such cases, muscles’ ability to generate forces reduces, thus affecting the joint’s torque production. Understanding how muscles generate forces is a key element to injury detection. Researchers have developed several computational methods to obtain muscle forces and joint torques. Electromyography (EMG) driven modeling is one of the approaches to estimate muscle forces and obtain joint torques from muscle activity measurements. Musculoskeletal models and EMG-driven models require necessary muscle-specific parameters for the calculation. The focus of this study is to investigate the EMG-driven approach along with an upper extremity musculoskeletal model to determine muscle forces of two major muscle groups, biceps brachii and triceps brachii, consisting of seven muscle-tendon units. Estimated muscle forces are used to determine the elbow joint torque. Experimental EMG signals and motion capture data are collected for a healthy subject. The musculoskeletal model is scaled to match the geometric parameters of the subject. Then, the approach calculates muscle forces and joint moment for two tasks: simple elbow flexion extension and triceps kickback. Individual muscle forces and net joint torques for both tasks are estimated. The study also has compared the effect of muscle-tendon parameters (optimal fiber length and tendon slack length) on the estimated results.

An Updated Model Does Not Reveal Sex Differences in Patellofemoral Joint Stress during Running.

Structure-specific loading may have implications in understanding the mechanisms of running related injury. As females demonstrate a prevalence of patellofemoral pain twice that of males, this may indicate differences in patellofemoral loads between males and females. Previous works investigating differences in patellofemoral joint stress have shown conflicting results, but the models employed have not used estimates of muscle forces or sex specific contact areas. The aim of this study was to examine sex differences in patellofemoral joint stress using an updated model to include estimates of quadriceps muscle force and sex-specific patellofemoral contact area. Descriptive Laboratory Study. Forty-five healthy recreational runners ran at a controlled speed down a 20-meter runway. Kinetic and kinematic data were utilized to estimate muscle forces using static optimization. Quadriceps muscle force was utilized with sex-specific patellofemoral joint contact area in a two-dimensional patellofemoral joint model to estimate patellofemoral joint stress. Multivariate tests were utilized to detect sex differences in patellofemoral loading and hip and knee kinematics. No differences were found between sexes in measures of patellofemoral loading or quadriceps force. Females displayed a reduced knee extension moment and greater hip adduction and internal rotation than males. The inclusion of static optimization to estimate quadriceps muscle force and sex-specific contact area of the patellofemoral joint did not reveal sex differences in patellofemoral joint stress, but differences in non-sagittal plane hip motion were detected. Therefore, two-dimensional patellofemoral models may not fully characterize differences in patellofemoral joint stress between males and females. Three-dimensional patellofemoral models may be necessary to determine if sex differences in patellofemoral joint stress exist. 3b.

Multi-level personalization of neuromusculoskeletal models to estimate physiologically plausible knee joint contact forces in children

Neuromusculoskeletal models are a powerful tool to investigate the internal biomechanics of an individual. However, commonly used neuromusculoskeletal models are generated via linear scaling of generic templates derived from elderly adult anatomies and poorly represent a child, let alone children with a neuromuscular disorder whose musculoskeletal structures and muscle activation patterns are profoundly altered. Model personalization can capture abnormalities and appropriately describe the underlying (altered) biomechanics of an individual. In this work, we explored the effect of six different levels of neuromusculoskeletal model personalization on estimates of muscle forces and knee joint contact forces to tease out the importance of model personalization for normal and abnormal musculoskeletal structures and muscle activation patterns. For six children, with and without cerebral palsy, generic scaled models were developed and progressively personalized by (1) tuning and calibrating musculotendon units’ parameters, (2) implementing an electromyogram-assisted approach to synthesize muscle activations, and (3) replacing generic anatomies with image-based bony geometries, and physiologically and physically plausible muscle kinematics. Biomechanical simulations of gait were performed in the OpenSim and CEINMS software on ten overground walking trials per participant. A mixed-ANOVA test, with Bonferroni corrections, was conducted to compare all models’ estimates. The model with the highest level of personalization produced the most physiologically plausible estimates. Model personalization is crucial to produce physiologically plausible estimates of internal biomechanical quantities. In particular, personalization of musculoskeletal anatomy and muscle activation patterns had the largest effect overall. Increased research efforts are needed to ease the creation of personalized neuromusculoskeletal models.

Feasibility and validity of a single camera CNN driven musculoskeletal model for muscle force estimation during upper extremity strength exercises: Proof-of-concept.

Muscle force analysis can be essential for injury risk estimation and performance enhancement in sports like strength training. However, current methods to record muscle forces including electromyography or marker-based measurements combined with a musculoskeletal model are time-consuming and restrict the athlete's natural movement due to equipment attachment. Therefore, the feasibility and validity of a more applicable method, requiring only a single standard camera for the recordings, combined with a deep-learning model and musculoskeletal model is evaluated in the present study during upper-body strength exercises performed by five athletes. Comparison of muscle forces obtained by the single camera driven model against those obtained from a state-of-the art marker-based driven musculoskeletal model revealed strong to excellent correlations and reasonable RMSD's of 0.4–2.1% of the maximum force (Fmax) for prime movers, and weak to strong correlations with RMSD's of 0.4–0.7% Fmax for stabilizing and secondary muscles. In conclusion, a single camera deep-learning driven model is a feasible method for muscle force analysis in a strength training environment, and first validity results show reasonable accuracies, especially for prime mover muscle forces. However, it is evident that future research should investigate this method for a larger sample size and for multiple exercises.

A Training Method for a Sensor-Based Exercise Rehabilitation Robot

In order to solve the problem that the traditional mirror therapy did not take into account the real-time recovery of the affected limb and the training effect was limited, a training method of sports rehabilitation robot based on sensor was proposed. A mirror active rehabilitation training system was proposed, which was composed of four steps including trajectory acquisition of the limb inertial measurement unit (IMU), fuzzy adaptive proportion differentiation (PD) control in closed-loop variable domain, muscle force estimation of the surface electromyographic signal (sEMG) of the affected limb, and power compensation of the outer ring of the affected limb. The experimental results showed that the sagittal forward flexion angle of the healthy shoulder increased from 0° to 128° at a relatively uniform speed, and the sagittal forward flexion angle of the shoulder was basically consistent with that of the healthy limb after the adaptive power compensation of the affected limb. The calculated trajectory tracking error of the healthy limb controlled by the fuzzy adaptive PD controller in the variable domain was 0.21 ± 1.35 ° . The horizontal backward extension angle of the healthy shoulder joint increased from 0° to 43°, and the following trajectory of the affected limb was roughly consistent with the movement trajectory of the healthy limb. The calculated tracking error of the healthy limb trajectory was 0.39 ± 1.45 ° . It was concluded that the control system could provide the real-time power compensation according to the recovery of the affected limb, give full play to the training initiative of the affected limb, and make the affected limb achieve a better rehabilitation training effect.

Sparse Sonomyography-Based Estimation of Isometric Force: A Comparison of Methods and Features

Noninvasive methods for estimation of joint and muscle forces have widespread clinical and research applications. Surface electromyography (sEMG) provides a measure of the neural activation of muscles which can be used to estimate the force produced by the muscle. However, sEMG based measures of force suffer from poor signal-to-noise ratio and limited spatiotemporal specificity. In this paper, we propose a sonomyography or ultrasound imaging-based offline approach for estimating continuous isometric force from a sparse set of ultrasound scanlines. Our approach isolates anatomically relevant features from A-mode scanlines isolated from B-mode images, thus greatly reducing the dimensionality of the feature space and the computational complexity involved in traditional ultrasound-based methods. We evaluate the performance of four regression methodologies for force prediction using the reduced sonomyographic feature set. We also evaluate the feasibility of a practical wearable sonomyography-based system by simulating the effect of transducer placement and varying the number of transducers used in force prediction. Our results demonstrate that Gaussian process regression models outperform other regression methods in predicting continuous force levels from just four equispaced transducers in offline settings. We believe that these findings will aid in the design of wearable, robust and computationally efficient sonomyography-based force prediction systems.