Estimate Muscle Forces Research Articles

Effects of repeated isometric and eccentric contractions on active muscle stiffness.

Joint stiffness endurance is considered essential in many sports events. We previously reported that reduced joint stiffness due to repetitive hopping was associated with reduced active muscle stiffness. However, the determinants of active muscle stiffness endurance were unknown. This study aimed to compare the effects of repeated isometric contractions (ISO), which induced metabolic muscle fatigue, and repeated eccentric contractions (ECC), which induced muscle damage, on active muscle stiffness endurance. Fourteen males performed two kinds of fatigue tasks (ISO and ECC) using only ankle joint. Before and after the fatigue tasks, changes in estimated muscle force and fascicle length during fast stretching were used to calculate the active muscle stiffness of the medial gastrocnemius muscle. In addition, the thickness of the plantar flexor muscles was measured before and after fatigue tasks. After fatigue tasks, no difference in the relative increase of muscle thickness was found between ISO and ECC. The increase in torque during fast stretching did not change after both ISO and ECC. The increase in fascicle length during fast stretching significantly increased after ECC but not ISO. Active muscle stiffness significantly decreased after ECC but not ISO. Active muscle stiffness decreased after repeated eccentric contractions damaging fascicles and did not change with repeated isometric contractions causing metabolic fatigue. These results implied that the joint stiffness reduction due to repetitive stretch-shortening cycle exercises shown in previous studies involved a reduction in active muscle stiffness due to repeated eccentric contractions.

A human digital twin approach for fatigue-aware task planning in human-robot collaborative assembly

Human-robot collaboration (HRC) has emerged as a pivotal paradigm in manufacturing, integrating the strengths of both human and robot capabilities. Neglecting human physical fatigue may adversely affect worker health and, in extreme cases, may lead to musculoskeletal disorders. However, human fatigue has rarely been considered for decision-making in HRC manufacturing systems. Integrating adaptive decision-making to optimise human fatigue in HRC manufacturing systems is crucial. Nonetheless, real-time perception and estimation of human fatigue and decision-making informed by human fatigue face considerable challenges. To address these challenges, this paper introduces a human digital twin method, a bidirectional communication system for physical fatigue assessment and reduction in human-robot collaborative assembly tasks. The methodology encompasses an IK-BiLSTM-AM-based surrogate model, which consists of inverse kinematics analysis (IK), bidirectional long short-term memory (BiLSTM), and attention mechanism (AM), for real-time muscle force estimation integrated with a muscle force-fatigue model for muscle fatigue assessment. An And-Or graph and optimisation model-based HRC task planner is also developed to alleviate physical fatigue via task allocation. The efficacy of this approach has been validated through proof-of-concept assembly experiments involving multiple subjects. The results show that the IK-BiLSTM-AM model achieves a minimum of 8% greater accuracy in muscle force estimation than the baseline methods. The 12-subject assessment results indicate that the task planner effectively reduces the physical fatigue of workers while performing collaborative assembly tasks.

Estimating muscle force based on a neuromuscular decoding approach adaptive to fatigue conditions

Electromyography (EMG) has been extensively employed for precise muscle force estimation. Although methods based on motor unit (MU) activities (the smallest functional unit of the neuromuscular control system) have achieved superior performance, their effectiveness in fatiguing conditions remains unknown. This study aims to investigate the performance of existing MU-based muscle force estimation methods in fatiguing conditions, including the representative neuromuscular decoding (NMD) method and firing rate (FR) −based method. Compared to FR-based method, the NMD method specifically accounts for the differences in force contributions among MUs and the force twitch response of muscle fibers. We conducted fatigue induction experiment to collect force and high-density surface EMG data from the abductor pollicis brevis muscles of eight subjects during their performance of thumb abduction. Subsequently, the analysis of myoelectric manifestation of fatigue was performed and four training–testing strategies were designed to examine the effect of fatigue on force estimation methods. Experimental results indicated that the NMD method achieved superior and stable performance (assessed through root mean square difference between estimated and actual force) across four strategies (p >0.05 revealed by ANOVA), with only slight performance decline when fatigue data was involved in training or/and testing phase. Conversely, fatigue caused significant performance degradation when using the FR-based and other five comparative methods (p <0.05). These findings demonstrate that the NMD approach is adaptive to fatiguing MU activities, which can provide precise and robust muscle force estimation against fatigue in motor control and human–machine interfaces.

Distinct Motion Control Strategy during Unanticipated Landing: Transitioning from Copers to Chronic Ankle Instability

Background: Patients with chronic ankle instability (CAI) demonstrated altered movement patterns during unanticipated landing compared to coper patients. Understanding the effects of kinematics, dynamics and energetics on individual movement patterns during landing could enhance motor control strategies for patients with ankle sprains while avoiding the transition of coper patients to CAI patients. Therefore, the purpose of this study was to investigate the differences in movement patterns of coper patients compared to CAI patients during the unanticipated landings; Methods: Fifteen individuals with CAI (age: 22.8±1.4 years; height: 180.1±4.2 cm; weight: 81.5±5.8 kg) and fifteen copers (age: 23.1±1.2 years; height: 179.8±4.4 cm, weight: 80.4±6.2 kg) participated in an unanticipated landing task, during which three-dimensional motion capture, ground reaction force (GRF), and muscle activation data were collected. A musculoskeletal model was used to estimate muscle force and joint power among these two groups. Joint power was calculated as the product of angular velocity in the sagittal plane and joint moment data, reflecting the energy transfer at the ankle, knee, and hip joints. Furthermore, energy dissipation and generation within these joints were determined by integrating specific regions of the joint power curve; Results: Individuals with CAI demonstrated a greater muscle force in the vastus lateralis compared copers during the unanticipated landing task, while copers exhibited higher peak muscle forces in the medial gastrocnemius (p=0.007), lateral gastrocnemius (p=0.002), soleus (p=0.004). The muscle activation patterns of CAI patients also differ from those of coper patients. Compared to copers, CAI patients exhibit earlier activation of the rectus femoris (p&lt;0.001) and lateral gastrocnemius muscles (p=0.042). Conversely, copers demonstrate earlier activation of the soleus (p=0.004) and medial gastrocnemius (p=0.003) muscles. In addition, joint power in CAI individuals during unanticipated landing shifted from the ankle to the knee and hip (p&lt;0.001); Conclusions: These findings suggest that individuals with CAI exhibit a change in motion control strategy during unanticipated landing tasks. The variations in peak forces and the ability of proximal muscles to generate force might enable them to offset the deficits noted in distal muscles. Energy redistribution issues observed in CAI patients may help to prevent the transition of coper patients towards developing CAI patients.

Synchronous gesture recognition and muscle force estimation based on piezoelectric micromachined ultrasound transducer

Early prosthetic control research mainly focused on motion intention recognition, but in recent years, study on control force has emerged. The simultaneous integration of motion intention recognition and control force estimation has significant practical value. This paper proposes an ultrasound control scheme using wearable device that enables synchronous intention recognition and accurate control force estimation. Eleven gestures abstracted from daily life are selected, with a force range of 0–100 % maximum voluntary contraction (MVC). The wearable device employed for muscle morphology monitoring consists of a piezoelectric micromachined ultrasound transducer (PMUT) and polymer encapsulation. It is small, lightweight, and eliminates the need for ultrasound gel, enhancing its practicality. Ultrasound echogenicity of two forearm muscles and corresponding output forces are simultaneously collected. We propose peak and location (PKL) feature and combine it with mean and standard deviation (MSD) feature to improve the accuracy of pattern recognition and force estimation. Experimental results show that during dynamic muscle contraction forces, the classification accuracy achieves 95.84 ± 1.48 %, while the synchronous estimation of forces yields a R2 correlation coefficient of 0.919 and a normalized root-mean-square deviation of 0.127. This scheme demonstrates the feasibility of PMUT-based ultrasound control for supporting synchronous gesture recognition and force estimation, and is expected to contribute to the practicality of prosthesis control.

Consensus for experimental design in electromyography (CEDE) project: Application of EMG to estimate muscle force

Skeletal muscles power movement. Deriving the forces produced by individual muscles has applications across various fields including biomechanics, robotics, and rehabilitation. Since direct in vivo measurement of muscle force in humans is invasive and challenging, its estimation through non-invasive methods such as electromyography (EMG) holds considerable appeal. This matrix, developed by the Consensus for Experimental Design in Electromyography (CEDE) project, summarizes recommendations on the use of EMG to estimate muscle force. The matrix encompasses the use of bipolar surface EMG, high density surface EMG, and intra-muscular EMG (1) to identify the onset of muscle force during isometric contractions, (2) to identify the offset of muscle force during isometric contractions, (3) to identify force fluctuations during isometric contractions, (4) to estimate force during dynamic contractions, and (5) in combination with musculoskeletal models to estimate force during dynamic contractions. For each application, recommendations on the appropriateness of using EMG to estimate force and justification for each recommendation are provided. The achieved consensus makes clear that there are limited scenarios in which EMG can be used to accurately estimate muscle forces. In most cases, it remains important to consider the activation as well as the muscle state and other biomechanical and physiological factors— such as in the context of a formal mechanical model. This matrix is intended to encourage interdisciplinary discussions regarding the integration of EMG with other experimental techniques and to promote advances in the application of EMG towards developing muscle models and musculoskeletal simulations that can accurately predict muscle forces in healthy and clinical populations.

Evaluating the Repeatability of Musculoskeletal Modelling Force Outcomes in Gait among Chronic Stroke Survivors: Implications for Contemporary Clinical Practice

This study aims to evaluate the consistency of musculoskeletal modelling outcomes during walking in chronic post-stroke patients, focusing on both affected and unaffected sides. Understanding the specific muscle forces involved is crucial for designing targeted rehabilitation strategies to improve balance and mobility after a stroke. Musculoskeletal modelling provides valuable insights into muscle and joint loading, aiding clinicians in analysing essential biomarkers and enhancing patients’ functional outcomes. However, the repeatability of these modelling outcomes in stroke gait has not been thoroughly explored until now. Twelve post-stroke, hemiparetic survivors were included in the study, which consisted of a gait analysis protocol to capture kinematic and kinetic variables. Two generic full body MSK models—Hamner (Ham) and Rajagopal (Raj)—were used to compute joint angles and muscle forces during walking, with combinations of two muscle force estimation algorithms (Static Optimisation (SO) and Computed Muscle Control (CMC)) and different joint degrees-of-freedoms (DOF). The multiple correlation coefficient (MCCoef) was used to compute repeatability for all forces, grouped based on anatomical function. Regardless of models and DOFs, the mean minimum (0.75) and maximum (0.94) MCCoefs denote moderate-to-excellent repeatability for all muscle groups. The combination of the Ham model and SO provided the most repeatable muscle force estimations of all the muscle groups except for the hip flexors, adductors and internal rotators. DOF configuration did not generally affect muscle force repeatability in the Ham–SO case, although the 311 seemed to relate to the highest values. Lastly, the DOF setting had a significant effect on some muscle groups’ force output, with the highest magnitudes reported for the 321 and 322 of non-paretic and paretic hip adductors and extensors, knee flexors and ankle dorsiflexors and paretic knee flexors. The primary findings of our study can assist users in selecting the most suitable modelling workflow and encourage the widespread adoption of MSK modelling in clinical practice.

MMG-Based Knee Dynamic Extension Force Estimation Using Cross-Talk and IGWO-LSTM.

Mechanomyography (MMG) is an important muscle physiological activity signal that can reflect the amount of motor units recruited as well as the contraction frequency. As a result, MMG can be utilized to estimate the force produced by skeletal muscle. However, cross-talk and time-series correlation severely affect MMG signal recognition in the real world. These restrict the accuracy of dynamic muscle force estimation and their interaction ability in wearable devices. To address these issues, a hypothesis that the accuracy of knee dynamic extension force estimation can be improved by using MMG signals from a single muscle with less cross-talk is first proposed. The hypothesis is then confirmed using the estimation results from different muscle signal feature combinations. Finally, a novel model (improved grey wolf optimizer optimized long short-term memory networks, i.e., IGWO-LSTM) is proposed for further improving the performance of knee dynamic extension force estimation. The experimental results demonstrate that MMG signals from a single muscle with less cross-talk have a superior ability to estimate dynamic knee extension force. In addition, the proposed IGWO-LSTM provides the best performance metrics in comparison to other state-of-the-art models. Our research is expected to not only improve the understanding of the mechanisms of quadriceps contraction but also enhance the flexibility and interaction capabilities of future rehabilitation and assistive devices.

Estimation of joint and muscle forces during exercise in various postures

An understanding of joint and muscle forces is essential for prescribing appropriate exercises for patients with musculoskeletal disorders. This study aimed to determine the joint and muscle forces during exercises in the sitting or supine posture. Ten healthy males (age: 25.4 ± 2.6 years) performed three standing exercises (gait, squat, and forward lunge) and three exercises in sitting or supine postures (knee extension while sitting, straight leg raise, and bridging). The joint and muscle forces of the lumbar spine and lower extremities were estimated using the musculoskeletal model simulation based on the motion capture data. In the analysis of the exercises in the sitting or supine postures, the external forces acting from the chair or floor on the body were estimated using the optimization algorithm. The hip and tibiofemoral joint force, as well as muscle force such as VL, GMAX, and GAS, exhibited significantly greater magnitudes during standing exercises. However, the L4-L5 joint force during bridging was equivalent to those during gait and squat. Bridging generated significantly larger muscle force in ES and MF than those during gait. Exercises performed in the sitting or supine postures induced a larger load on L4-L5 and hip joint and trunk extensor muscle forces than exercises in the standing posture. While the joint and muscle forces were generally larger during standing rather than sitting or supine exercises, certain notable exceptions were observed, such as bridging exercise. It suggested that physical therapists should use caution when performing supine exercise on patients with low back pain.

Effect of the ischial support on muscle force estimation during transfemoral walking.

Transmission of loads between the prosthetic socket and the residual limb is critical for the comfort and walking ability of people with transfemoral amputation. This transmission is mainly determined by the socket tightening, muscle forces, and socket ischial support. However, numerical investigations of the amputated gait, using modeling approaches such as MusculoSkeletal (MSK) modeling, ignore the weight-bearing role of the ischial support. This simplification may lead to errors in the muscle force estimation. This study aims to propose a MSK model of the amputated gait that accounts for the interaction between the body and the ischial support for the estimation of the muscle forces of 13 subjects with unilateral transfemoral amputation. Contrary to previous studies on the amputated gait which ignored the interaction with the ischial support, here, the contact on the ischial support was included in the external loads acting on the pelvis in a MSK model of the amputated gait. Including the ischial support induced an increase in the activity of the main abductor muscles, while adductor muscles' activity was reduced. These results suggest that neglecting the interaction with the ischial support leads to erroneous muscle force distribution considering the gait of people with transfemoral amputation. Although subjects with various bone geometries, particularly femur lengths, were included in the study, similar results were obtained for all subjects. Eventually, the estimation of muscle forces from MSK models could be used in combination with finite element models to provide quantitative data for the design of prosthetic sockets.

Cranial functional specialisation for strength precedes morphological evolution in Oviraptorosauria

Oviraptorosaurians were a theropod dinosaur group that reached high diversity in the Late Cretaceous. Within oviraptorosaurians, the later diverging oviraptorids evolved distinctive crania which were extensively pneumatised, short and tall, and had a robust toothless beak, interpreted as providing a powerful bite for their herbivorous to omnivorous diet. The present study explores the ability of oviraptorid crania to resist large mechanical stresses compared with other theropods and where this adaptation originated within oviraptorosaurians. Digital 3D cranial models were constructed for the earliest diverging oviraptorosaurian, Incisivosaurus gauthieri, and three oviraptorids, Citipati osmolskae, Conchoraptor gracilis, and Khaan mckennai. Finite element analyses indicate oviraptorosaurian crania were stronger than those of other herbivorous theropods (Erlikosaurus and Ornithomimus) and were more comparable to the large, carnivorous Allosaurus. The cranial biomechanics of Incisivosaurus align with oviraptorids, indicating an early establishment of distinctive strengthened cranial biomechanics in Oviraptorosauria, even before the highly modified oviraptorid cranial morphology. Bite modelling, using estimated muscle forces, suggests oviraptorid crania may have functioned closer to structural safety limits. Low mechanical stresses around the beaks of oviraptorids suggest a convergently evolved, functionally distinct rhamphotheca, serving as a cropping/feeding tool rather than for stress reduction, when compared with other herbivorous theropods.

Estimation of lower back muscle force in a lifting task using wearable IMUs

Low back pain is commonly reported in occupational settings due to factors such as heavy lifting and poor ergonomic practices, often resulting in significant healthcare expenses and lowered productivity. Assessment tools for human motion and ergonomic risk at the workplace are still limited. Therefore, this study aimed to assess lower back muscle and joint reaction forces in laboratory conditions using wearable inertial measurement units (IMUs) during weight lifting, a frequently high-risk workplace task. Ten able-bodied participants were instructed to lift a 28 lbs. box while surface electromyography sensors, IMUs, and a camera-based motion capture system recorded their muscle activity and body motion. The data recorded by IMUs and motion capture system were used to estimate lower back muscle and joint reaction forces via musculoskeletal modeling. Lower back muscle patterns matched well with electromyography recordings. The normalized mean absolute differences between muscle forces estimated based on measurements of IMUs and cameras were less than 25 %, and the statistical parametric mapping results indicated no significant difference between the forces estimated by both systems. However, abrupt changes in motion, such as lifting initiation, led to significant differences (p < 0.05) between the muscle forces. Furthermore, the maximum L5-S1 joint reaction force estimated using IMU data was significantly lower (p < 0.05) than those estimated by cameras during weight lifting and lowering. The study showed how kinematic errors from IMUs propagated through the musculoskeletal model and affected the estimations of muscle forces and joint reaction forces. Our findings showed the potential of IMUs for in-field ergonomic risk evaluations.

A Physics-Informed Low-Shot Adversarial Learning for sEMG-Based Estimation of Muscle Force and Joint Kinematics.

Muscle force and joint kinematics estimation from surface electromyography (sEMG) are essential for real-time biomechanical analysis of the dynamic interplay among neural muscle stimulation, muscle dynamics, and kinetics. Recent advances in deep neural networks (DNNs) have shown the potential to improve biomechanical analysis in a fully automated and reproducible manner. However, the small sample nature and physical interpretability of biomechanical analysis limit the applications of DNNs. This paper presents a novel physics-informed low-shot adversarial learning method for sEMG-based estimation of muscle force and joint kinematics. This method seamlessly integrates Lagrange's equation of motion and inverse dynamic muscle model into the generative adversarial network (GAN) framework for structured feature decoding and extrapolated estimation from the small sample data. Specifically, Lagrange's equation of motion is introduced into the generative model to restrain the structured decoding of the high-level features following the laws of physics. A physics-informed policy gradient is designed to improve the adversarial learning efficiency by rewarding the consistent physical representation of the extrapolated estimations and the physical references. Experimental validations are conducted on two scenarios (i.e. the walking trials and wrist motion trials). Results indicate that the estimations of the muscle forces and joint kinematics are unbiased compared to the physics-based inverse dynamics, which outperforms the selected benchmark methods, including physics-informed convolution neural network (PI-CNN), vallina generative adversarial network (GAN), and multi-layer extreme learning machine (ML-ELM).

Estimation of Muscle Forces of Lower Limbs Based on CNN-LSTM Neural Network and Wearable Sensor System.

Estimation of vivo muscle forces during human motion is important for understanding human motion control mechanisms and joint mechanics. This paper combined the advantages of the convolutional neural network (CNN) and long-short-term memory (LSTM) and proposed a novel muscle force estimation method based on CNN-LSTM. A wearable sensor system was also developed to collect the angles and angular velocities of the hip, knee, and ankle joints in the sagittal plane during walking, and the collected kinematic data were used as the input for the neural network model. In this paper, the muscle forces calculated using OpenSim based on the Static Optimization (SO) method were used as the standard value to train the neural network model. Four lower limb muscles of the left leg, including gluteus maximus (GM), rectus femoris (RF), gastrocnemius (GAST), and soleus (SOL), were selected as the studying objects in this paper. The experiment results showed that compared to the standard CNN and the standard LSTM, the CNN-LSTM performed better in muscle forces estimation under slow (1.2 m/s), medium (1.5 m/s), and fast walking speeds (1.8 m/s). The average correlation coefficients between true and estimated values of four muscle forces under slow, medium, and fast walking speeds were 0.9801, 0.9829, and 0.9809, respectively. The average correlation coefficients had smaller fluctuations under different walking speeds, which indicated that the model had good robustness. The external testing experiment showed that the CNN-LSTM also had good generalization. The model performed well when the estimated object was not included in the training sample. This article proposed a convenient method for estimating muscle forces, which could provide theoretical assistance for the quantitative analysis of human motion and muscle injury. The method has established the relationship between joint kinematic signals and muscle forces during walking based on a neural network model; compared to the SO method to calculate muscle forces in OpenSim, it is more convenient and efficient in clinical analysis or engineering applications.

Muscle contributions to reduced ankle joint contact force during drop vertical jumps in patients with chronic ankle instability

Chronic ankle instability is a condition linked to progressive early ankle joint degeneration. Patients with chronic ankle instability exhibit altered biomechanics during gait and jump landings and these alterations are believed to contribute to aberrant joint loading and subsequent joint degeneration. Musculoskeletal modeling has the capacity to estimate joint loads from individual muscle forces. However, the influence of chronic ankle instability on joint contact forces remains largely unknown. The objective of this study was to compare tri-axial (i.e., compressive, anterior-posterior, and medial–lateral) ankle joint contact forces between those with and without chronic ankle instability during the ground contact phase of a drop vertical jump. Fifteen individuals with and 15 individuals without chronic ankle instability completed drop vertical jump maneuvers in a research laboratory. We used those data to drive three-dimensional musculoskeletal simulations and estimate muscle forces and tri-axial joint contact force variables (i.e., peak and impulse). Compared to those without chronic ankle instability, the ankles of patients with chronic ankle instability underwent lower compressive ankle joint contact forces as well as lower anterior-posterior and medial–lateral shearing forces during the weight acceptance phase of landing (p <.05). These findings suggest that patients with chronic ankle instability exhibit lower ankle joint loading patterns than uninjured individuals during a drop vertical jump, which may be considered in rehabilitation to potentially reduce the risk of early onset of ankle joint degeneration.

A wearable gait lab powered by sensor-driven digital twins for quantitative biomechanical analysis post-stroke.

Commonly, quantitative gait analysis post-stroke is performed in fully equipped laboratories housing costly technologies for quantitative evaluation of a patient's movement capacity. Combining such technologies with an electromyography (EMG)-driven musculoskeletal model can estimate muscle force properties non-invasively, offering clinicians insights into motor impairment mechanisms. However, lab-constrained areas and time-demanding sensor setup and data processing limit the practicality of these technologies in routine clinical care. We presented wearable technology featuring a multi-channel EMG-sensorized garment and an automated muscle localization technique. This allows unsupervised computation of muscle-specific activations, combined with five inertial measurement units (IMUs) for assessing joint kinematics and kinetics during various walking speeds. Finally, the wearable system was combined with a person-specific EMG-driven musculoskeletal model (referred to as human digital twins), enabling the quantitative assessment of movement capacity at a muscle-tendon level. This human digital twin facilitates the estimation of ankle dorsi-plantar flexion torque resulting from individual muscle-tendon forces. Results demonstrate the wearable technology's capability to extract joint kinematics and kinetics. When combined with EMG signals to drive a musculoskeletal model, it yields reasonable estimates of ankle dorsi-plantar flexion torques (R 2=0.65±0.21) across different walking speeds for post-stroke individuals. Notably, EMG signals revealing an individual's control strategy compensate for inaccuracies in IMU-derived kinetics and kinematics when input into a musculoskeletal model. Our proposed wearable technology holds promise for estimating muscle kinetics and resulting joint torque in time-limited and space-constrained environments. It represents a crucial step toward translating human movement biomechanics outside of controlled lab environments for effective motor impairment monitoring.

An artificial neural network for full-body posture prediction in dynamic lifting activities and effects of its prediction errors on model-estimated spinal loads

Musculoskeletal models have indispensable applications in occupational risk assessment/management and clinical treatment/rehabilitation programs. To estimate muscle forces and joint loads, these models require body posture during the activity under consideration. Posture is usually measured via video-camera motion tracking approaches that are time-consuming, costly, and/or limited to laboratories. Alternatively, posture-prediction tools based on artificial intelligence can be trained using measured postures of several subjects performing many activities. We aimed to use our previous posture-prediction artificial neural network (ANN), developed based on many measured static postures, to predict posture during dynamic lifting activities. Moreover, effects of the ANN posture-prediction errors on dynamic spinal loads were investigated using subject-specific musculoskeletal models. Seven individuals each performed twenty-five lifting tasks while their full-body three-dimensional posture was measured by a 10-camera Vicon system and also predicted by the ANN as functions of the hand-load positions during the lifting activities. The measured and predicted postures (i.e., coordinates of 39 skin markers) and their model-estimated L5-S1 loads were compared. The overall root-mean-squared-error (RMSE) and normalized (by the range of measured values) RMSE (nRMSE) between the predicted and measured postures for all markers/tasks/subjects was equal to 7.4 cm and 4.1 %, respectively (R2 = 0.98 and p < 0.05). The model-estimated L5-S1 loads based on the predicted and measured postures were generally in close agreements as also confirmed by the Bland-Altman analyses; the nRMSE for all subjects/tasks was < 10 % (R2 > 0.7 and p > 0.05). In conclusion, the easy-to-use ANN can accurately predict posture in dynamic lifting activities and its predicted posture can drive musculoskeletal models.

Mid-Activity and At-Home Wearable Bioimpedance Elucidates an Interpretable Digital Biomarker of Muscle Fatigue.

Muscle health and decreased muscle performance (fatigue) quantification has proven to be an invaluable tool for both athletic performance assessment and injury prevention. However, existing methods estimating muscle fatigue are infeasible for everyday use. Wearable technologies are feasible for everyday use and can enable discovery of digital biomarkers of muscle fatigue. Unfortunately, the current state-of-the-art wearable systems for muscle fatigue tracking suffer from either low specificity or poor usability. We propose using dual-frequency bioimpedance analysis (DFBIA) to non-invasively assess intramuscular fluid dynamics and thereby muscle fatigue. A wearable DFBIA system was developed to measure leg muscle fatigue of 11 individuals during a 13-day protocol consisting of exercise and unsupervised at-home portions. We derived a digital biomarker of muscle fatigue, fatigue score, from the DFBIA signals that was able to estimate the percent reduction in muscle force during exercise with repeated-measures Pearson's r = 0.90 and mean absolute error (MAE) of 3.6%. This fatigue score also estimated delayed onset muscle soreness with repeated-measures Pearson's r = 0.83 and MAE = 0.83. Using at-home data, DFBIA was strongly associated with absolute muscle force of participants (n = 198, p < 0.001). These results demonstrate the utility of wearable DFBIA for non-invasively estimating muscle force and pain through the changes in intramuscular fluid dynamics. The presented approach may inform development of future wearable systems for quantifying muscle health and provide a novel framework for athletic performance optimization and injury prevention.

Individual Muscle Force Estimation in the Human Forearm Using Multi-Muscle MR Elastography (MM-MRE).

To establish the sensitivity of magnetic resonance elastography (MRE) to active muscle contraction in multiple muscles of the forearm. We combined MRE of forearm muscles with an MRI-compatible device, the MREbot, to simultaneously measure the mechanical properties of tissues in the forearm and the torque applied by the wrist joint during isometric tasks. We measured shear wave speed of thirteen forearm muscles via MRE in a series of contractile states and wrist postures and fit these outputs to a force estimation algorithm based on a musculoskeletal model. Shear wave speed changed significantly upon several factors, including whether the muscle was recruited as an agonist or antagonist (p = 0.0019), torque amplitude (p = <0.0001), and wrist posture (p = 0.0002). Shear wave speed increased significantly during both agonist (p = <0.0001) and antagonist (p = 0.0448) contraction. Additionally, there was a greater increase in shear wave speed at greater levels of loading. The variations due to these factors indicate the sensitivity to functional loading of muscle. Under the assumption of a quadratic relationship between shear wave speed and muscle force, MRE measurements accounted for an average of 70% of the variance in the measured joint torque. This study shows the ability of MM-MRE to capture variations in individual muscle shear wave speed due to muscle activation and presents a method to estimate individual muscle force through MM-MRE derived measurements of shear wave speed. MM-MRE could be used to establish normal and abnormal muscle co-contraction patterns in muscles of the forearm controlling hand and wrist function.

Reliability of lower leg muscle elasticity using shear wave elastography in non-weight-bearing and weight-bearing

PurposeMuscle elasticity can be quantified with shear wave elastography (SWE) and has been used as an estimate of muscle force but reliability has not been established for lower leg muscles. The purpose of this study was to examine the intra-rater and inter-rater reliability of elasticity measures in non-weight-bearing (NWB) and weight-bearing (WB) for the tibialis anterior (TA), tibialis posterior (TP), peroneal longus (PL), and peroneal brevis (PB) muscles using SWE. MethodsA total of 109 recreationally active healthy adults participated. The study employed a single-cohort, same-day repeated-measures test–retest design. Elasticity, measured in kilopascals as the Young’s modulus, was converted to the shear modulus. All four muscles were measured in NWB and at 90% WB. ResultsIntra-rater reliability estimates were good to excellent for NWB (ICC = 0.930–0.988) and WB (ICC = 0.877–0.978) measures. Inter-rater reliability estimates were moderate to good (ICC = 0.500–0.795) for NWB measures and poor to good (ICC = 0.346–0.910) for WB measures. ConclusionDespite the studies poor to good inter-rater variability, the intra-rater reproducibility represents the potential benefit of SWE in NWB and WB. Establishing the reliability of SWE with clinical and biomechanical approaches may aid in improved understanding of the mechanical properties of muscle.