Static optimization underestimates antagonist muscle activity at the glenohumeral joint: A musculoskeletal modeling study

Muscle and joint function in the rotator cuff deficient shoulder

Full-thickness rotator cuff tears can lead to poor coaptation of the humeral head to the glenoid, disrupting muscle forces required for glenohumeral joint stability, ultimately leading to joint subluxation. The aim of this study was to evaluate muscle forces and glenohumeral joint translations during elevation in the presence of isolated and combined full-thickness rotator cuff tears. Eight fresh-frozen upper limbs were mounted to a computer-controlled testing apparatus that simulated joint motion by simulated muscle force application. Scapular-plane abduction was performed, and glenohumeral joint translations were measured using an optoelectronic system. Testing was performed in the native shoulder, a following an isolated tear to the supraspinatus, as well as combined tears involving the supraspinatus and subscapularis, as well as supraspinatus, infraspinatus, and teres minor. Rotator cuff tears significantly increased middle deltoid force at 30°, 60°, and 90° of abduction relative to that in the native shoulder (p < 0.05). Significantly greater superior translations were observed relative to the intact shoulder due to combined tears to the supraspinatus and infraspinatus at 30° of abduction (mean increase: 1.6 mm, p = 0.020) and 60° of abduction (mean increase: 4.8 mm, p = 0.040). This study illustrates the infraspinatus-teres minor complex as a major humeral head depressor and contributor to glenohumeral joint stability. An increase in deltoid force during abduction occurs in the presence of rotator cuff tears, which exacerbates superior migration of the humeral head. The findings may help in the development of clinical tests in rotator cuff tear diagnostics, in surgical planning of rotator cuff repair, and in planning of targeted rehabilitation.

Evaluation of finite element modeling methods for predicting compression screw failure in a custom pelvic implant

Introduction: Three-dimensional (3D)-printed custom pelvic implants have become a clinically viable option for patients undergoing pelvic cancer surgery with resection of the hip joint. However, increased clinical utilization has also necessitated improved implant durability, especially with regard to the compression screws used to secure the implant to remaining pelvic bone. This study evaluated six different finite element (FE) screw modeling methods for predicting compression screw pullout and fatigue failure in a custom pelvic implant secured to bone using nine compression screws. Methods: Three modeling methods (tied constraints (TIE), bolt load with constant force (BL-CF), and bolt load with constant length (BL-CL)) generated screw axial forces using functionality built into Abaqus FE software; while the remaining three modeling methods (isotropic pseudo-thermal field (ISO), orthotropic pseudo-thermal field (ORT), and equal-and-opposite force field (FOR)) generated screw axial forces using iterative physics-based relationships that can be implemented in any FE software. The ability of all six modeling methods to match specified screw pretension forces and predict screw pullout and fatigue failure was evaluated using an FE model of a custom pelvic implant with total hip replacement. The applied hip contact forces in the FE model were estimated at two locations in a gait cycle. For each of the nine screws in the custom implant FE model, likelihood of screw pullout failure was predicted using maximum screw axial force, while likelihood of screw fatigue failure was predicted using maximum von Mises stress. Results: The three iterative physics-based modeling methods and the non-iterative Abaqus BL-CL method produced nearly identical predictions for likelihood of screw pullout and fatigue failure, while the other two built-in Abaqus modeling methods yielded vastly different predictions. However, the Abaqus BL-CL method required the least computation time, largely because an iterative process was not needed to induce specified screw pretension forces. Of the three iterative methods, FOR required the fewest iterations and thus the least computation time. Discussion: These findings suggest that the BL-CL screw modeling method is the best option when Abaqus is used for predicting screw pullout and fatigue failure in custom pelvis prostheses, while the iterative physics-based FOR method is the best option if FE software other than Abaqus is used.

The moment arms and lines of action of subscapularis after the Latarjet procedure

The Latarjet procedure is an established surgical treatment for recurrent glenohumeral joint instability with glenoid bone loss. Intraoperatively, the conjoint tendon and its attachement on the coracoid bone graft is routed through a split in subscapularis where the graft is fixed to and augments the anteroinferior glenoid. The objective of this in vitro study was to quantify the influence of glenohumeral joint position and conjoint tendon force on the lines of action and moment arms of subscapularis muscle sub-regions after Latarjet surgery. Eight fresh-frozen, entire upper extremities were mounted onto a testing apparatus, and a cable-pulley system was used to apply physiological muscle loading to the major shoulder muscles. The lines of action and moment arms of four subregions of subscapularis (superior, mid-superior, mid-inferior, and inferior) were quantified radiographically with the conjoint tendon unloaded and loaded while the shoulder was in (i) 0° abduction (ii) 90° abduction (iii) 90° abduction and full external rotation (ABER), and (iv) the apprehension position, defined as ABER with 30° horizontal extension. Conjoint tendon loading after Latarjet surgery significantly increased the inferior inclination of the lines of action of the mid-inferior and inferior subregions of subscapularis in the scapular plane in ABER and apprehension positions (p < 0.001), as well as decreased the horizontal flexion moment arm of the inferior subscapularis (p = 0.040). Increased subscapularis inferior inclination may ultimately increase inferior joint shear potential, while smaller horizontal flexion leverage may reduce joint flexion capacity. The findings have implications for Latarjet surgical planning and postoperative rehabilitation prescription.

Muscle Strength Identification Based on Isokinetic Testing and Spine Musculoskeletal Modeling

Subject-specific spinal musculoskeletal modeling can help understand the spinal loading mechanism during human locomotion. However, existing literature lacks methods to identify the maximum isometric strength of individual spinal muscles. In this study, a muscle strength identification method combining isokinetic testing and musculoskeletal simulations was proposed, and the influence of muscle synergy and intra-abdominal pressure (IAP) on identified spinal muscle strength was further discussed. A multibody dynamic model of the spinal musculoskeletal system was established and controlled by a feedback controller. Muscle strength parameters were adjusted based on the measured isokinetic moments, and muscle synergy vectors and the IAP piston model were further introduced. The results of five healthy subjects showed that the proposed method successfully identified the subject-specific spinal flexor/extensor strength. Considering the synergistic activations of antagonist muscles improved the correlation between the simulated and measured spinal moments, and the introduction of IAP slightly increased the identified spinal extensor strength. The established method is beneficial for understanding spinal loading distributions for athletes and patients with sarcopenia.

Effect of patient-specific scapular morphology on the glenohumeral joint force and shoulder muscle force equilibrium: a study of rotator cuff tear and osteoarthritis patients

Introduction: Osteoarthritis (OA) and rotator cuff tear (RCT) pathologies have distinct scapular morphologies that impact disease progression. Previous studies examined the correlation between scapular morphology and glenohumeral joint biomechanics through critical shoulder angle (CSA) variations. In abduction, higher CSAs, common in RCT patients, increase vertical shear force and rotator cuff activation, while lower CSAs, common in OA patients, are associated with higher compressive force. However, the impact of the complete patient-specific scapular morphology remains unexplored due to challenges in establishing personalized models. Methods: CT data of 48 OA patients and 55 RCT patients were collected. An automated pipeline customized the AnyBody™ model with patient-specific scapular morphology and glenohumeral joint geometry. Biomechanical simulations calculated glenohumeral joint forces and instability ratios (shear-to-compressive forces). Moment arms and torques of rotator cuff and deltoid muscles were analyzed for each patient-specific geometry. Results and discussion: This study confirms the increased instability ratio on the glenohumeral joint in RCT patients during abduction (mean maximum is 32.80% higher than that in OA), while OA patients exhibit a higher vertical instability ratio in flexion (mean maximum is 24.53% higher than that in RCT) due to the increased inferior vertical shear force. This study further shows lower total joint force in OA patients than that in RCT patients (mean maximum total force for the RCT group is 11.86% greater than that for the OA group), attributed to mechanically advantageous muscle moment arms. The findings highlight the significant impact of the glenohumeral joint center positioning on muscle moment arms and the total force generated. We propose that the RCT pathomechanism is related to force magnitude, while the OA pathomechanism is associated with the shear-to-compressive loading ratio. Overall, this research contributes to the understanding of the impact of the complete 3D scapular morphology of the individual on shoulder biomechanics.

Psoas force recruitment in full-body musculoskeletal movement simulations is restored with a geometrically informed cost function weighting

Simulations of musculoskeletal models are useful for estimating internal muscle and joint forces. However, predicted forces rely on optimization and modeling formulations. Geometric detail is important to predict muscle forces, and greater geometric complexity is required for muscles that have broad attachments or span many joints, as in the torso. However, the extent to which optimized muscle force recruitment is sensitive to these geometry choices is unclear. We developed level, uphill and downhill sloped walking simulations using a standard (uniformly weighted, "fatigue-like") cost function with lower limb and full-body musculoskeletal models to evaluate hip muscle recruitment with different geometric representations of the psoas muscle under walking conditions with varying hip moment demands. We also tested a novel cost function formulation where muscle activations were weighted according to the modeled geometric detail in the full-body model. Total psoas force was less and iliacus, rectus femoris, and other hip flexors' force was greater when psoas was modeled with greater geometric detail compared to other hip muscles for all slopes. The proposed weighting scheme restored hip muscle force recruitment without sacrificing detailed psoas geometry. In addition, we found that lumbar, but not hip, joint contact forces were influenced by psoas force recruitment. Our results demonstrate that static optimization dependent simulations using models comprised of muscles with different amounts of geometric detail bias force recruitment toward muscles with less geometric detail. Muscle activation weighting that accounts for differences in geometric complexity across muscles corrects for this recruitment bias.

Comparison of Synergy Extrapolation and Static Optimization for Estimating Multiple Unmeasured Muscle Activations during Walking

Background

Calibrated electromyography (EMG)-driven musculoskeletal models can provide great insight into internal quantities (e.g., muscle forces) that are difficult or impossible to measure experimentally. However, the need for EMG data from all involved muscles presents a significant barrier to the widespread application of EMG-driven modeling methods. Synergy extrapolation (SynX) is a computational method that can estimate a single missing EMG signal with reasonable accuracy during the EMG-driven model calibration process, yet its performance in estimating a larger number of missing EMG signals remains unclear.

Methods

This study assessed the accuracy with which SynX can use eight measured EMG signals to estimate muscle activations and forces associated with eight missing EMG signals in the same leg during walking while simultaneously performing EMG-driven model calibration. Experimental gait data collected from two individuals post-stroke, including 16 channels of EMG data per leg, were used to calibrate an EMG-driven musculoskeletal model, providing "gold standard" muscle activations and forces for evaluation purposes. SynX was then used to predict the muscle activations and forces associated with the eight missing EMG signals while simultaneously calibrating EMG-driven model parameter values. Due to its widespread use, static optimization (SO) was also utilized to estimate the same muscle activations and forces. Estimation accuracy for SynX and SO was evaluated using root mean square errors (RMSE) to quantify amplitude errors and correlation coefficient r values to quantify shape similarity, each calculated with respect to "gold standard" muscle activations and forces.

Results

On average, SynX produced significantly more accurate amplitude and shape estimates for unmeasured muscle activations (RMSE 0.08 vs. 0.15 , r value 0.55 vs. 0.12) and forces (RMSE 101.3 N vs. 174.4   N , r value 0.53 vs. 0.07) compared to SO. SynX yielded calibrated Hill-type muscle-tendon model parameter values for all muscles and activation dynamics model parameter values for measured muscles that were similar to "gold standard" calibrated model parameter values.

Conclusions

These findings suggest that SynX could make it possible to calibrate EMG-driven musculoskeletal models for all important lower-extremity muscles with as few as eight carefully chosen EMG signals and eventually contribute to the design of personalized rehabilitation and surgical interventions for mobility impairments.

Subacromial contact after acromioplasty in the rotator cuff deficient shoulder

Subacromial impingement (SAI) is associated with shoulder pain and dysfunction and is exacerbated by rotator cuff tears; however, the role of acromioplasty in mitigating subacromial contact in the rotator cuff deficient shoulder remains debated. This study aimed to quantify the influence of isolated and combined tears involving the supraspinatus on subacromial contact during abduction; and second, to evaluate the influence of acromioplasty on joint space size and subacromial contact under these pathological conditions. Eight fresh-frozen human cadaveric upper limbs were mounted to a computer-controlled testing apparatus that simulated joint motion by simulated force application. Shoulder abduction was performed while three-dimensional joint kinematics was measured using an optoelectronic system, and subacromial contact evaluated using a digital pressure sensor secured to the inferior acromion. Testing was performed after an isolated tear to the supraspinatus, as well as tears involving the subscapularis and infraspinatus-teres minor, both before and after acromioplasty. Rotator cuff tears significantly increased peak subacromial pressure (p < 0.001), average subacromial pressure (p = 0.001), and contact force (p = 0.034) relative to those in the intact shoulder. Following acromioplasty, significantly lower peak subacromial contact pressure, force and area were observed for all rotator cuff tears involving the supraspinatus at 30° of abduction (p < 0.05). Acromioplasty predominantly reduces acromion thickness anteriorly thereby reducing subacromial contact in the rotator cuff deficient shoulder, particularly in early to mid-abduction where superior glenohumeral joint shear force potential is large. These findings provide a biomechanical basis for acromioplasty as an intervention for SAI syndrome and as an adjunct to rotator cuff repairs.

Upper Extremity Muscle Activation Pattern Prediction Through Synergy Extrapolation and Electromyography-Driven Modeling

Patients with neuromuscular disease fail to produce necessary muscle force and have trouble maintaining joint moment required to perform activities of daily living. Measuring muscle force values in patients with neuromuscular disease is important but challenging. Electromyography (EMG) can be used to obtain muscle activation values, which can be converted to muscle forces and joint torques. Surface electrodes can measure activations of superficial muscles, but fine-wire electrodes are needed for deep muscles, although it is invasive and require skilled personnel and preparation time. EMG-driven modeling with surface electrodes alone could underestimate the net torque. In this research, authors propose a methodology to predict muscle activations from deeper muscles of the upper extremity. This method finds missing muscle activation one at a time by combining an EMG-driven musculoskeletal model and muscle synergies. This method tracks inverse dynamics joint moments to determine synergy vector weights and predict muscle activation of selected shoulder and elbow muscles of a healthy subject. In addition, muscle-tendon parameter values (optimal fiber length, tendon slack length, and maximum isometric force) have been personalized to the experimental subject. The methodology is tested for a wide range of rehabilitation tasks of the upper extremity across multiple healthy subjects. Results show this methodology can determine single unmeasured muscle activation up to Pearson's correlation coefficient (R) of 0.99 (root mean squared error, RMSE = 0.001) and 0.92 (RMSE = 0.13) for the elbow and shoulder muscles, respectively, for one degree-of-freedom (DoF) tasks. For more complicated five DoF tasks, activation prediction accuracy can reach up to R = 0.71 (RMSE = 0.29).

Predicted vs. measured paraspinal muscle activity in adolescent idiopathic scoliosis patients: EMG validation of optimization-based musculoskeletal simulations

Musculoskeletal (MSK) models offer great potential for predicting the muscle forces required to inform more detailed simulations of vertebral endplate loading in adolescent idiopathic scoliosis (AIS). In this work, simulations based on static optimization were compared with in vivo measurements in two AIS patients to determine whether computational approaches alone are sufficient for accurate prediction of paraspinal muscle activity during functional activities. We used biplanar radiographs and marker-based motion capture, ground reaction force, and electromyography (EMG) data from two patients with mild and moderate thoracolumbar AIS (Cobb angles: 21° and 45°, respectively) during standing while holding two weights in front (reference position), walking, running, and object lifting. Using a fully automated approach, 3D spinal shape was extracted from the radiographs. Geometrically personalized OpenSim-based MSK models were created by deforming the spine of pre-scaled full-body models of children/adolescents. Simulations were performed using an experimentally controlled backward approach. Differences between model predictions and EMG measurements of paraspinal muscle activity (both expressed as a percentage of the reference position values) at three different locations around the scoliotic main curve were quantified by root mean square error (RMSE) and cross-correlation (XCorr). Predicted and measured muscle activity correlated best for mild AIS during object lifting (XCorr's ≥ 0.97), with relatively low RMSE values. For moderate AIS as well as the walking and running activities, agreement was lower, with XCorr reaching values of 0.51 and comparably high RMSE values. This study demonstrates that static optimization alone seems not appropriate for predicting muscle activity in AIS patients, particularly in those with more than mild deformations as well as when performing upright activities such as walking and running.

Does enforcing glenohumeral joint stability matter? A new rapid muscle redundancy solver highlights the importance of non-superficial shoulder muscles

The complexity of the human shoulder girdle enables the large mobility of the upper extremity, but also introduces instability of the glenohumeral (GH) joint. Shoulder movements are generated by coordinating large superficial and deeper stabilizing muscles spanning numerous degrees-of-freedom. How shoulder muscles are coordinated to stabilize the movement of the GH joint remains widely unknown. Musculoskeletal simulations are powerful tools to gain insights into the actions of individual muscles and particularly of those that are difficult to measure. In this study, we analyze how enforcement of GH joint stability in a musculoskeletal model affects the estimates of individual muscle activity during shoulder movements. To estimate both muscle activity and GH stability from recorded shoulder movements, we developed a Rapid Muscle Redundancy (RMR) solver to include constraints on joint reaction forces (JRFs) from a musculoskeletal model. The RMR solver yields muscle activations and joint forces by minimizing the weighted sum of squared-activations, while matching experimental motion. We implemented three new features: first, computed muscle forces include active and passive fiber contributions; second, muscle activation rates are enforced to be physiological, and third, JRFs are efficiently formulated as linear functions of activations. Muscle activity from the RMR solver without GH stability was not different from the computed muscle control (CMC) algorithm and electromyography of superficial muscles. The efficiency of the solver enabled us to test over 3600 trials sampled within the uncertainty of the experimental movements to test the differences in muscle activity with and without GH joint stability enforced. We found that enforcing GH stability significantly increases the estimated activity of the rotator cuff muscles but not of most superficial muscles. Therefore, a comparison of shoulder model muscle activity to EMG measurements of superficial muscles alone is insufficient to validate the activity of rotator cuff muscles estimated from musculoskeletal models.

A multi-resolution physics-informed recurrent neural network: formulation and application to musculoskeletal systems

This work presents a multi-resolution physics-informed recurrent neural network (MR PI-RNN), for simultaneous prediction of musculoskeletal (MSK) motion and parameter identification of the MSK systems. The MSK application was selected as the model problem due to its challenging nature in mapping the high-frequency surface electromyography (sEMG) signals to the low-frequency body joint motion controlled by the MSK and muscle contraction dynamics. The proposed method utilizes the fast wavelet transform to decompose the mixed frequency input sEMG and output joint motion signals into nested multi-resolution signals. The prediction model is subsequently trained on coarser-scale input-output signals using a gated recurrent unit (GRU), and then the trained parameters are transferred to the next level of training with finer-scale signals. These training processes are repeated recursively under a transfer-learning fashion until the full-scale training (i.e., with unfiltered signals) is achieved, while satisfying the underlying dynamic equilibrium. Numerical examples on recorded subject data demonstrate the effectiveness of the proposed framework in generating a physics-informed forward-dynamics surrogate, which yields higher accuracy in motion predictions of elbow flexion-extension of an MSK system compared to the case with single-scale training. The framework is also capable of identifying muscle parameters that are physiologically consistent with the subject's kinematics data.

Influence of femoral anteversion angle and neck-shaft angle on muscle forces and joint loading during walking

Femoral deformities, e.g. increased or decreased femoral anteversion (AVA) and neck-shaft angle (NSA), can lead to pathological gait patterns, altered joint loads, and degenerative joint diseases. The mechanism how femoral geometry influences muscle forces and joint load during walking is still not fully understood. The objective of our study was to investigate the influence of femoral AVA and NSA on muscle forces and joint loads during walking. We conducted a comprehensive musculoskeletal modelling study based on three-dimensional motion capture data of a healthy person with a typical gait pattern. We created 25 musculoskeletal models with a variety of NSA (93°-153°) and AVA (-12°-48°). For each model we calculated moment arms, muscle forces, muscle moments, co-contraction indices and joint loads using OpenSim. Multiple regression analyses were used to predict muscle activations, muscle moments, co-contraction indices, and joint contact forces based on the femoral geometry. We found a significant increase in co-contraction of hip and knee joint spanning muscles in models with increasing AVA and NSA, which led to a substantial increase in hip and knee joint contact forces. Decreased AVA and NSA had a minor impact on muscle and joint contact forces. Large AVA lead to increases in both knee and hip contact forces. Large NSA (153°) combined with large AVA (48°) led to increases in hip joint contact forces by five times body weight. Low NSA (108° and 93°) combined with large AVA (48°) led to two-fold increases in the second peak of the knee contact forces. Increased joint contact forces in models with increased AVA and NSA were linked to changes in hip muscle moment arms and compensatory increases in hip and knee muscle forces. Knowing the influence of femoral geometry on muscle forces and joint loads can help clinicians to improve treatment strategies in patients with femoral deformities.

The History and Future of Neuromusculoskeletal Biomechanics

The Executive Council of the International Society of Biomechanics has initiated and overseen the commemorations of the Society's 50th Anniversary in 2023. This included multiple series of lectures at the ninth World Congress of Biomechanics in 2022 and XXIXth Congress of the International Society of Biomechanics in 2023, all linked to special issues of International Society of Biomechanics' affiliated journals. This special issue of the Journal of Applied Biomechanics is dedicated to the biomechanics of the neuromusculoskeletal system. The reader is encouraged to explore this special issue which comprises 6 papers exploring the current state-of the-art, and future directions and roles for neuromusculoskeletal biomechanics. This editorial presents a very brief history of the science of the neuromusculoskeletal system's 4 main components: the central nervous system, musculotendon units, the musculoskeletal system, and joints, and how they biomechanically integrate to enable an understanding of the generation and control of human movement. This also entails a quick exploration of contemporary neuromusculoskeletal biomechanics and its future with new fields of application.

EMG-Informed Neuromusculoskeletal Modelling Estimates Muscle Forces and Joint Moments During Electrical Stimulation

This study implemented an electromyogram (EMG)-informed neuromusculoskeletal (NMS) model evaluating the volitional contributions to muscle forces and joint moments during functional electrical stimulation (FES). The NMS model was calibrated using motion and EMG (biceps brachii and triceps brachii) data recorded from able-bodied participants (n=3) performing weighted elbow flexion and extension cycling movements while equipped with an EMG-controlled closed-loop FES system. Models were executed using three computational approaches (i) EMG-driven, (ii) EMG-hybrid and (iii) EMG-assisted to estimate muscle forces and joint moments. Both EMG-hybrid and EMG-assisted modes were able estimate the elbow moment (root mean squared error and coefficient of determination), but the EMG-hybrid method also enabled quantifying the volitional contributions to muscle forces and elbow moments during FES. The proposed modelling method allows for assessing volitional contributions of patients to muscle force during FES rehabilitation, and could be used as biomarkers of recovery, biofeedback, and for real-time control of combined FES and robotic systems.

Validation of a musculoskeletal model to investigate hip joint mechanics in response to dynamic multiplanar tasks

Existing hip-focused musculoskeletal (MSK) models are limited by the hip range of motion, hip musculature detail, or have only been qualitatively validated. The purposes of this study were to: i) modify the existing 2396Hip MSK model to simulate dynamic tasks with multiplanar hip joint motion; and ii) validate the modified MSK model quantitatively against experimental data. Experimental data was collected from five healthy adults (age = 25 [6] years, two females) during eight movement tasks. The motion and ground reaction force data were input into the MSK modeling software OpenSim to calculate muscle activations and hip contact forces (HCFs). The HCFs were compared to experimental HCFs previously measured in total hip arthroplasty (THA) patients using instrumented hip prostheses. A gait simulation was performed using data from one THA patient to directly assess the model's accuracy in estimating HCFs. The young adults' modeled and experimental muscle activations for seven muscles were compared using a cross-correlation function. The model only overestimated the peak resultant HCFs by 0.06-0.08 N/BW compared to the experimentally measured HCFs of the THA patient. The young adults' HCFs were over two standard deviations higher than previously measured in the THA patients, which is likely a result of different movement patterns. The correlation coefficients indicated strong correlations between experimental and modeled muscle activations in 50 of the 56 comparisons. The results of this study suggest the new MSK model is an appropriate method to quantify HCFs and muscle activations in response to dynamic, multiplanar tasks among young, healthy adults.

Ex vivo experimental strategies for assessing unconstrained shoulder biomechanics: A scoping review

BACKGROUND

Biomechanical studies of the shoulder often choose an ex vivo approach, especially when investigating the active and passive contribution of individual muscles. Although various simulators of the glenohumeral joint and its muscles have been developed, to date a testing standard has not been established. The objective of this scoping review was to present an overview of methodological and experimental studies describing ex vivo simulators that assess unconstrained, muscular driven shoulder biomechanics.

METHODS

All studies with ex vivo or mechanical simulation experiments using an unconstrained glenohumeral joint simulator and active components mimicking the muscles were included in this scoping review. Static experiments and humeral motion imposed through an external guide, e.g., a robotic device, were excluded.

RESULTS

Nine different glenohumeral simulators were identified in 51 studies after the screening process. We identified four control strategies characterized by: (a) using a primary loader to determine the secondary loaders with constant force ratios; (b) using variable muscle force ratios according to electromyography; (c) calibrating the muscle path profile and control each motor according to this profile; or (d) using muscle optimization.

CONCLUSION

The simulators with the control strategy (b) (n = 1) or (d) (n = 2) appear most promising due to its capability to mimic physiological muscle loads.

Latarjet's muscular alterations increase glenohumeral joint stability: A theoretical study

The surgical Latarjet procedure aims to stabilise the glenohumeral joint following anterior dislocations. Despite restoring joint stability, the procedure introduces alterations of muscle paths which likely modify the shoulder dynamics. Currently, these altered muscular functions and their implications are unclear. Hence, this work aims to predict changes in muscle lever arms, muscle and joint forces following a Latarjet procedure by using a computational approach. Planar shoulder movements of ten participants were experimentally assessed. A validated upper-limb musculoskeletal model was utilised in two configurations, i.e., a baseline model, simulating normal joint, and a Latarjet model simulating its related muscular alterations. Muscle lever arms and differences in muscle and joint forces between models were derived from the experimental marker data and static optimisation technique. Lever arms of most altered muscles, hence their role, were substantially changed after Latarjet. Altered muscle forces varied by up to 15% of the body weight. Total glenohumeral joint force increased by up to 14% of the body weight after Latarjet, mostly due to increase in compression force. Our simulation indicated that the Latarjet muscular alterations lead to changes in the muscular recruitment and contribute to the stability of the glenohumeral joint by increasing compression force during planar motions.

Maintaining soldier musculoskeletal health using personalised digital humans, wearables and/or computer vision

OBJECTIVES

The physical demands of military service place soldiers at risk of musculoskeletal injuries and are major concerns for military capability. This paper outlines the development new training technologies to prevent and manage these injuries.

DESIGN

Narrative review.

METHODS

Technologies suitable for integration into next-generation training devices were examined. We considered the capability of technologies to target tissue level mechanics, provide appropriate real-time feedback, and their useability in-the-field.

RESULTS

Musculoskeletal tissues' health depends on their functional mechanical environment experienced in military activities, training and rehabilitation. These environments result from the interactions between tissue motion, loading, biology, and morphology. Maintaining health of and/or repairing joint tissues requires targeting the "ideal" in vivo tissue mechanics (i.e., loading and strain), which may be enabled by real-time biofeedback. Recent research has shown that these biofeedback technologies are possible by integrating a patient's personalised digital twin and wireless wearable devices. Personalised digital twins are personalised neuromusculoskeletal rigid body and finite element models that work in real-time by code optimisation and artificial intelligence. Model personalisation is crucial in obtaining physically and physiologically valid predictions.

CONCLUSIONS

Recent work has shown that laboratory-quality biomechanical measurements and modelling can be performed outside the laboratory with a small number of wearable sensors or computer vision methods. The next stage is to combine these technologies into well-designed easy to use products.

Video-driven simulation of lower limb mechanical loading during aquatic exercises

Understanding the mechanical demands of an exercise on the musculoskeletal system is crucial to prescribe effective training or therapeutic interventions. Yet, that knowledge is currently limited in water, mostly because of the difficulty in evaluating external resistance. Here I reconcile recent advances in 3D markerless pose and mesh estimation, biomechanical simulations, and hydrodynamic modeling, to predict lower limb mechanical loading during aquatic exercises. Simulations are driven exclusively from a single video. Fluid forces were estimated within 12.5±4.1% of the peak forces determined through computational fluid dynamics analyses, at a speed three orders of magnitude greater. In silico hip and knee resultant joint forces agreed reasonably well with in vivo instrumented implant recordings (R²=0.74) downloaded from the OrthoLoad database, both in magnitude (RMSE =251±125 N) and direction (cosine similarity = 0.92±0.09). Hip flexors, glutes, adductors, and hamstrings were the main contributors to hip joint compressive forces (40.4±12.7%, 25.6±9.7%, 14.2±4.8%, 13.0±8.2%, respectively), while knee compressive forces were mostly produced by the gastrocnemius (39.1±15.9%) and vasti (29.4±13.7%). Unlike dry-land locomotion, non-hip- and non-knee-spanning muscles provided little to no offloading effect via dynamic coupling. This noninvasive method has the potential to standardize the reporting of exercise intensity, inform the design of rehabilitation protocols and improve their reproducibility.

Muscle coordination during archery shooting: A comparison of archers with different skill levels

This study aimed to compare the muscle coordination of different skill level archers by using the concept of muscle synergies. A total of 28 archers (8 elite, 12 mid-level, and 8 novices) were recruited to participate in this study. Electromyography (EMG) signals were recorded using a 13-channel (Trigno EMG sensor, Delsys Inc., USA) wireless surface EMG system. Fundamental synergies containing time-dependent activation coefficients (motor primitives) and time-invariant muscle weightings (motor modules) were extracted using non-negative matrix factorisation. We observed three fundamental synergies in all groups during archery shooting. The results showed that the centre of activity of the motor primitive of synergy-3 occurred earlier in novice archers than in elite and mid-level archers, which was followed by an increase in the averaged frequency of overlaps. The results also showed a slight difference in the relative muscle contribution of the motor module of synergy-2 and -3 within different groups. These findings revealed that the number of muscle synergies did not depend on the proficiency level; however, more expert archers improved timing management better than less experienced ones. Therefore, we suggest that coaches and athletes focus on optimising the temporal coordination of the follow-through phase.

A Feature-Encoded Physics-Informed Parameter Identification Neural Network for Musculoskeletal Systems

Identification of muscle-tendon force generation properties and muscle activities from physiological measurements, e.g., motion data and raw surface electromyography (sEMG), offers opportunities to construct a subject-specific musculoskeletal (MSK) digital twin system for health condition assessment and motion prediction. While machine learning approaches with capabilities in extracting complex features and patterns from a large amount of data have been applied to motion prediction given sEMG signals, the learned data-driven mapping is black-box and may not satisfy the underlying physics and has reduced generality. In this work, we propose a feature-encoded physics-informed parameter identification neural network (FEPI-PINN) for simultaneous prediction of motion and parameter identification of human MSK systems. In this approach, features of high-dimensional noisy sEMG signals are projected onto a low-dimensional noise-filtered embedding space for the enhancement of forwarding dynamics prediction. This FEPI-PINN model can be trained to relate sEMG signals to joint motion and simultaneously identify key MSK parameters. The numerical examples demonstrate that the proposed framework can effectively identify subject-specific muscle parameters and the trained physics-informed forward-dynamics surrogate yields accurate motion predictions of elbow flexion-extension motion that are in good agreement with the measured joint motion data.

The Number and Structure of Muscle Synergies Depend on the Number of Recorded Muscles: A Pilot Simulation Study with OpenSim

The muscle synergy approach is used to evaluate motor control and to quantitatively determine the number and structure of the modules underlying movement. In experimental studies regarding the upper limb, typically 8 to 16 EMG probes are used depending on the application, although the number of muscles involved in motor generation is higher. Therefore, the number of motor modules may be underestimated and the structure altered with the standard spatial synergy model based on the non-negative matrix factorization (NMF). In this study, we compared the number and structure of muscle synergies when considering 12 muscles (an "average" condition that represents previous studies) and 32 muscles of the upper limb, also including multiple muscle heads and deep muscles. First, we estimated the muscle activations with an upper-limb model in OpenSim using data from multi-directional reaching movements acquired in experimental sessions; then, spatial synergies were extracted from EMG activations from 12 muscles and from 32 muscles and their structures were compared. Finally, we compared muscle synergies obtained from OpenSim and from real experimental EMG signals to assess the reliability of the results. Interestingly, we found that on average, an additional synergy is needed to reconstruct the same R² level with 32 muscles with respect to 12 muscles; synergies have a very similar structure, although muscles with comparable physiological functions were added to the synergies extracted with 12 muscles. The additional synergies, instead, captured patterns that could not be identified with only 12 muscles. We concluded that current studies may slightly underestimate the number of controlled synergies, even though the main structure of synergies is not modified when adding more muscles. We also show that EMG activations estimated with OpenSim are in partial (but not complete) agreement with experimental recordings. These findings may have significative implications for motor control and clinical studies.

Effect of Expertise on Shoulder and Upper Limb Kinematics, Electromyography, and Estimated Muscle Forces During a Lifting Task

OBJECTIVE

To highlight the working strategies used by expert manual handlers compared with novice manual handlers, based on recordings of shoulder and upper limb kinematics, electromyography (EMG), and estimated muscle forces during a lifting task.

BACKGROUND

Novice workers involved in assembly, manual handling, and personal assistance tasks are at a higher risk of upper limb musculoskeletal disorders (MSDs). However, few studies have investigated the effect of expertise on upper limb exposure during workplace tasks.

METHOD

Sixteen experts in manual handling and sixteen novices were equipped with 10 electromyographic electrodes to record shoulder muscle activity during a manual handling task consisting of lifting a box (8 or 12 kg), instrumented with three six-axis force sensors, from hip to eye level. Three-dimensional trunk and upper limb kinematics, hand-to-box contact forces, and EMG were recorded. Then, joint contributions, activation levels, and muscle forces were calculated and compared between groups.

RESULTS

Sternoclavicular-acromioclavicular joint contributions were higher in experts at the beginning of the movement, and in novices at the end, whereas the opposite was observed for the glenohumeral joint. EMG activation levels were 37% higher for novices but predicted muscle forces were higher in experts.

CONCLUSION

This study highlights significant differences between experts and novices in shoulder kinematics, EMG, and muscle forces; hence, providing effective work guidelines to ensure the development of a safe handling strategy is important.

APPLICATION

Shoulder kinematics, EMG, and muscle forces could be used as ergonomic tools to identify inappropriate techniques that could increase the prevalence of shoulder injuries.

Electromyography measurements of the deep hip muscles do not improve estimates of hip contact force

The deep hip muscles are often omitted in studies investigating hip contact forces using neuromusculoskeletal modelling methods. However, recent evidence indicates the deep hip muscles have potential to change the direction of hip contact force and could have relevance for hip contact loading estimates. Further, it is not known whether deep hip muscle excitation patterns can be accurately estimated using neuromusculoskeletal modelling or require measurement (through invasive and time-consuming methods) to inform models used to estimate hip contact forces. We calculated hip contact forces during walking, squatting, and squat-jumping for 17 participants using electromyography (EMG)-informed neuromusculoskeletal modelling with (informed) and without (synthesized) intramuscular EMG for the deep hip muscles (piriformis, obturator internus, quadratus femoris). Hip contact force magnitude and direction, calculated as the angle between hip contact force and vector from femoral head to acetabular center, were compared between configurations using a paired t-test deployed through statistical parametric mapping (P < 0.05). Additionally, root mean square error, correlation coefficients (R²), and timing accuracy between measured and modelled deep hip muscle excitation patterns were computed. No significant between-configuration differences in hip contact force magnitude or direction were found for any task. However, the synthesized method poorly predicted (R²-range 0.02-0.3) deep hip muscle excitation patterns for all tasks. Consequently, intramuscular EMG of the deep hip muscles may be unnecessary when estimating hip contact force magnitude or direction using EMG-informed neuromusculoskeletal modelling, though is likely essential for investigations of deep hip muscle function.

In Vitro Simulation of Shoulder Motion Driven by Three-Dimensional Scapular and Humeral Kinematics

In vitro simulation of three-dimensional (3D) shoulder motion using in vivo kinematics obtained from human subjects allows investigation of clinical conditions in the context of physiologically relevant biomechanics. Herein, we present a framework for laboratory simulation of subject-specific kinematics that combines individual 3D scapular and humeral control in cadavers. The objectives were to: (1) robotically simulate seven healthy subject-specific 3D scapulothoracic and glenohumeral kinematic trajectories in six cadavers, (2) characterize system performance using kinematic orientation accuracy and repeatability, and muscle force repeatability metrics, and (3) analyze effects of input kinematics and cadaver specimen variability. Using an industrial robot to orient the scapula range of motion (ROM), errors with repeatability of ±0.1 mm and <0.5 deg were achieved. Using a custom robot and a trajectory prediction algorithm to orient the humerus relative to the scapula, orientation accuracy for glenohumeral elevation, plane of elevation, and axial rotation of <3 deg mean absolute error (MAE) was achieved. Kinematic accuracy was not affected by varying input kinematics or cadaver specimens. Muscle forces over five repeated setups showed variability typically <33% relative to the overall simulations. Varying cadaver specimens and subject-specific human motions showed effects on muscle forces, illustrating that the system was capable of differentiating changes in forces due to input conditions. The anterior and middle deltoid, specifically, showed notable variations in patterns across the ROM that were affected by subject-specific motion. This machine provides a platform for future laboratory studies to investigate shoulder biomechanics and consider the impacts of variable input kinematics from populations of interest, as they can significantly impact study outputs and resultant conclusions.

Application of Wearable Sensors in Actuation and Control of Powered Ankle Exoskeletons: A Comprehensive Review

Powered ankle exoskeletons (PAEs) are robotic devices developed for gait assistance, rehabilitation, and augmentation. To fulfil their purposes, PAEs vastly rely heavily on their sensor systems. Human–machine interface sensors collect the biomechanical signals from the human user to inform the higher level of the control hierarchy about the user’s locomotion intention and requirement, whereas machine–machine interface sensors monitor the output of the actuation unit to ensure precise tracking of the high-level control commands via the low-level control scheme. The current article aims to provide a comprehensive review of how wearable sensor technology has contributed to the actuation and control of the PAEs developed over the past two decades. The control schemes and actuation principles employed in the reviewed PAEs, as well as their interaction with the integrated sensor systems, are investigated in this review. Further, the role of wearable sensors in overcoming the main challenges in developing fully autonomous portable PAEs is discussed. Finally, a brief discussion on how the recent technology advancements in wearable sensors, including environment—machine interface sensors, could promote the future generation of fully autonomous portable PAEs is provided.

Electromyography-Assisted Neuromusculoskeletal Models Can Estimate Physiological Muscle Activations and Joint Moments Across the Neck Before Impacts

Knowledge of neck muscle activation strategies before sporting impacts is crucial for investigating mechanisms of severe spinal injuries. However, measurement of muscle activations during impacts is experimentally challenging and computational estimations are not often guided by experimental measurements. We investigated neck muscle activations before impacts with the use of electromyography (EMG)-assisted neuromusculoskeletal models. Kinematics and EMG recordings from four major neck muscles of a rugby player were experimentally measured during rugby activities. A subject-specific musculoskeletal model was created with muscle parameters informed from MRI measurements. The model was used in the calibrated EMG-informed neuromusculoskeletal modeling toolbox and three neural solutions were compared: (i) static optimization (SO), (ii) EMG-assisted (EMGa), and (iii) MRI-informed EMG-assisted (EMGaMRI). EMGaMRI and EMGa significantly (p < 0.01) outperformed SO when tracking cervical spine net joint moments from inverse dynamics in flexion/extension (RMSE = 0.95, 1.14, and 2.32 N·m) but not in lateral bending (RMSE = 1.07, 2.07, and 0.84 N·m). EMG-assisted solutions generated physiological muscle activation patterns and maintained experimental cocontractions significantly (p < 0.01) outperforming SO, which was characterized by saturation and nonphysiological "on-off" patterns. This study showed for the first time that physiological neck muscle activations and cervical spine net joint moments can be estimated without assumed a priori objective criteria before impacts. Future studies could use this technique to provide detailed initial loading conditions for theoretical simulations of neck injury during impacts.

Dynamic Spatial Tuning Patterns of Shoulder Muscles with Volunteers in a Driving Posture

Computational human body models (HBMs) of drivers for pre-crash simulations need active shoulder muscle control, and volunteer data are lacking. The goal of this paper was to build shoulder muscle dynamic spatial tuning patterns, with a secondary focus to present shoulder kinematic evaluation data. 8M and 9F volunteers sat in a driver posture, with their torso restrained, and were exposed to upper arm dynamic perturbations in eight directions perpendicular to the humerus. A dropping 8-kg weight connected to the elbow through pulleys applied the loads; the exact timing and direction were unknown. Activity in 11 shoulder muscles was measured using surface electrodes, and upper arm kinematics were measured with three cameras. We found directionally specific muscle activity and presented dynamic spatial tuning patterns for each muscle separated by sex. The preferred directions, i.e. the vector mean of a spatial tuning pattern, were similar between males and females, with the largest difference of 31° in the pectoralis major muscle. Males and females had similar elbow displacements. The maxima of elbow displacements in the loading plane for males was 189 ± 36 mm during flexion loading, and for females, it was 196 ± 36 mm during adduction loading. The data presented here can be used to design shoulder muscle controllers for HBMs and evaluate the performance of shoulder models.

The effectiveness of EMG-driven neuromusculoskeletal model calibration is task dependent

Calibration of neuromusculoskeletal models using functional tasks is performed to calculate subject-specific musculotendon parameters, as well as coefficients describing the shape of muscle excitation and activation functions. The objective of the present study was to employ a neuromusculoskeletal model of the shoulder driven entirely from muscle electromyography (EMG) to quantify the influence of different model calibration strategies on muscle and joint force predictions. Three healthy adults performed dynamic shoulder abduction and flexion, followed by calibration tasks that included reaching, head touching as well as active and passive abduction, flexion and axial rotation, and submaximal isometric abduction, flexion and axial rotation contractions. EMG data were simultaneously measured from 16 shoulder muscles using surface and intramuscular electrodes, and joint motion evaluated using video motion analysis. Muscle and joint forces were calculated using subject-specific EMG-driven neuromusculoskeletal models that were uncalibrated and calibrated using (i) all calibration tasks (ii) sagittal plane calibration tasks, and (iii) scapular plane calibration tasks. Joint forces were compared to published instrumented implant data. Calibrating models across all tasks resulted in glenohumeral joint force magnitudes that were more similar to instrumented implant data than those derived from any other model calibration strategy. Muscles that generated greater torque were more sensitive to calibration than those that contributed less. This study demonstrates that extensive model calibration over a broad range of contrasting tasks produces the most accurate and physiologically relevant musculotendon and EMG-to-activation parameters. This study will assist in development and deployment of subject-specific neuromusculoskeletal models.

The deep hip muscles are unlikely to contribute to hip stability in the sagittal plane during walking: a stiffness approach

- Objective: This study determined whether the deep hip muscles could contribute to hip stability.

METHODS

Hip stability was defined as rotational hip stiffness in the sagittal plane, which was calculated for walking trials for 12 participants via electromyography (EMG)informed neuromusculoskeletal modelling that included all 22 hip spanning muscles. Three model configurations were compared that differed in the excitations of the deep hip muscles, but were identical in the excitations of all other muscles: (1) deep hip muscles informed by intramuscular EMG measurements (assisted activation); (2) deep hip muscles with simulated zero activation (no activation); (3) deep hip muscles with simulated maximal activation (maximal activation). Sagittal plane rotational hip stiffness over the gait cycle was compared between model configurations using a within-participant analysis of variance via statistical parametric mapping (p<0.05).

RESULTS

Compared to the assisted activation configuration, hip stiffness (mean (95% confidence interval)) was 0.8% (0.7 to 0.9) lower in the no activation configuration, and 3.2% (2.9 to 3.5) higher in the maximal activation configuration over the gait cycle.

CONCLUSION

Regardless of activation level, the deep hip muscles made little contribution to sagittal plane rotational hip stiffness, which casts uncertainty around their assumed function as hip stabilizers.

SIGNIFICANCE

The merit of targeted deep hip muscle strengthening to improve hip stability in rehabilitation programs for remains unclear.

Intramuscular EMG-Driven Musculoskeletal Modelling: Towards Implanted Muscle Interfacing in Spinal Cord Injury Patients

OBJECTIVE

Surface EMG-driven modelling has been proposed as a means to control assistive devices by estimating joint torques. Implanted EMG sensors have several advantages over wearable sensors but provide a more localized information on muscle activity, which may impact torque estimates. Here, we tested and compared the use of surface and intramuscular EMG measurements for the estimation of required assistive joint torques using EMG driven modelling.

METHODS

Four healthy subjects and three incomplete spinal cord injury (SCI) patients performed walking trials at varying speeds. Motion capture marker trajectories, surface and intramuscular EMG, and ground reaction forces were measured concurrently. Subject-specific musculoskeletal models were developed for all subjects, and inverse dynamics analysis was performed for all individual trials. EMG-driven modelling based joint torque estimates were obtained from surface and intramuscular EMG.

RESULTS

The correlation between the experimental and predicted joint torques was similar when using intramuscular or surface EMG as input to the EMG-driven modelling estimator in both healthy individuals and patients.

CONCLUSION

We have provided the first comparison of non-invasive and implanted EMG sensors as input signals for torque estimates in healthy individuals and SCI patients.

SIGNIFICANCE

Implanted EMG sensors have the potential to be used as a reliable input for assistive exoskeleton joint torque actuation.

Simulating the effect of glenohumeral capsulorrhaphy on kinematics and muscle function

This study aimed to use a predictive simulation framework to examine shoulder kinematics, muscular effort, and task performance during functional upper limb movements under simulated selective glenohumeral capsulorrhaphy. A musculoskeletal model of the torso and upper limb was adapted to include passive restraints that simulated the changes in shoulder range of motion stemming from selective glenohumeral capsulorrhaphy procedures (anteroinferior, anterosuperior, posteroinferior, posterosuperior, and total anterior, inferior, posterior, and superior). Predictive muscle-driven simulations of three functional movements (upward reach, forward reach, and head touch) were generated with each model. Shoulder kinematics (elevation, elevation plane, and axial rotation), muscle cost (i.e., muscular effort), and task performance time were compared to a baseline model to assess the impact of the capsulorrhaphy procedures. Minimal differences in shoulder kinematics and task performance times were observed, suggesting that task performance could be maintained across the capsulorrhaphy conditions. Increased muscle cost was observed under the selective capsulorrhaphy conditions, however this was dependent on the task and capsulorrhaphy condition. Larger increases in muscle cost were observed under the capsulorrhaphy conditions that incurred the greatest reductions in shoulder range of motion (i.e., total inferior, total anterior, anteroinferior, and total posterior conditions) and during tasks that required shoulder kinematics closer to end range of motion (i.e., upward reach and head touch). The elevated muscle loading observed could present a risk to joint capsule repair. Appropriate rehabilitation following glenohumeral capsulorrhaphy is required to account for the elevated demands placed on muscles, particularly when a significant range of motion loss presents.

Fusing Accelerometry with Videography to Monitor the Effect of Fatigue on Punching Performance in Elite Boxers

Wearable sensors and motion capture technology are accepted instruments to measure spatiotemporal variables during punching performance and to study the externally observable effects of fatigue. This study aimed to develop a computational framework enabling three-dimensional inverse dynamics analysis through the tracking of punching kinematics obtained from inertial measurement units and uniplanar videography. The framework was applied to six elite male boxers performing a boxing-specific punch fatigue protocol. OpenPose was used to label left side upper-limb landmarks from which sagittal plane kinematics were computed. Custom-made inertial measurement units were embedded into the boxing gloves, and three-dimensional punch accelerations were analyzed using statistical parametric mapping to evaluate the effects of both fatigue and laterality. Tracking simulations of a sub-set of left-handed punches were formulated as optimal control problems and converted to nonlinear programming problems for solution with a trapezoid collocation method. The laterality analysis revealed the dominant side fatigued more than the non-dominant, while tracking simulations revealed shoulder abduction and elevation moments increased across the fatigue protocol. In future, such advanced simulation and analysis could be performed in ecologically valid contexts, whereby multiple inertial measurement units and video cameras might be used to model a more complete set of dynamics.

Targeted Achilles Tendon Training and Rehabilitation Using Personalized and Real-Time Multiscale Models of the Neuromusculoskeletal System

Musculoskeletal tissues, including tendons, are sensitive to their mechanical environment, with both excessive and insufficient loading resulting in reduced tissue strength. Tendons appear to be particularly sensitive to mechanical strain magnitude, and there appears to be an optimal range of tendon strain that results in the greatest positive tendon adaptation. At present, there are no tools that allow localized tendon strain to be measured or estimated in training or a clinical environment. In this paper, we first review the current literature regarding Achilles tendon adaptation, providing an overview of the individual technologies that so far have been used in isolation to understand in vivo Achilles tendon mechanics, including 3D tendon imaging, motion capture, personalized neuromusculoskeletal rigid body models, and finite element models. We then describe how these technologies can be integrated in a novel framework to provide real-time feedback of localized Achilles tendon strain during dynamic motor tasks. In a proof of concept application, Achilles tendon localized strains were calculated in real-time for a single subject during walking, single leg hopping, and eccentric heel drop. Data was processed at 250 Hz and streamed on a smartphone for visualization. Achilles tendon peak localized strains ranged from ∼3 to ∼11% for walking, ∼5 to ∼15% during single leg hop, and ∼2 to ∼9% during single eccentric leg heel drop, overall showing large strain variation within the tendon. Our integrated framework connects, across size scales, knowledge from isolated tendons and whole-body biomechanics, and offers a new approach to Achilles tendon rehabilitation and training. A key feature is personalization of model components, such as tendon geometry, material properties, muscle geometry, muscle-tendon paths, moment arms, muscle activation, and movement patterns, all of which have the potential to affect tendon strain estimates. Model personalization is important because tendon strain can differ substantially between individuals performing the same exercise due to inter-individual differences in these model components.

Sex differences in upper limb musculoskeletal biomechanics during a lifting task

Women experience higher prevalence of work-related upper limb musculoskeletal disorders compared to men. Previous studies have investigated the biological, kinematic and electromyographic sex-related differences during a lifting task but the actual differences in musculoskeletal loads remain unknown. We investigated the sex differences in three musculoskeletal indicators: the sum of muscle activations, the sum of muscle forces and the relative time spent beyond a shear-compression dislocation ratio. A musculoskeletal model was scaled on 20 women and 20 men lifting a 6 or 12kg box from hip to eye level. Women generated more muscle forces and activations than men, regardless of the lifted mass. Those differences occurred when the box was above shoulder level. In addition, women might spend more time beyond a shear-compression dislocation ratio. Our work suggests higher musculoskeletal loads among women compared to men during a lifting task, which could be the result of poor technique and strength difference.

EMG-Assisted Algorithm to Account for Shoulder Muscles Co-Contraction in Overhead Manual Handling

Glenohumeral stability is essential for a healthy function of the shoulder. It is ensured partly by the scapulohumeral muscular balance. Accordingly, modelling muscle interactions is a key factor in the understanding of occupational pathologies, and the development of ergonomic interventions. While static optimization is commonly used to estimate muscle activations, it tends to underestimate the role of shoulder’s antagonist muscles. The purpose of this study was to implement experimental electromyographic (EMG) data to predict muscle activations that could account for the stabilizing role of the shoulder muscles. Kinematics and EMG were recorded from 36 participants while lifting a box from hip to eye level. Muscle activations and glenohumeral joint reactions were estimated using an EMG-assisted algorithm and compared to those obtained using static optimization with a generic and calibrated model. Muscle activations predicted with the EMG-assisted method were generally larger. Additionally, more interactions between the different rotator cuff muscles, as well as between primer actuators and stabilizers, were predicted with the EMG-assisted method. Finally, glenohumeral forces calculated from a calibrated model remained within the boundaries of the glenoid stability cone. These findings suggest that EMG-assisted methods could account for scapulohumeral muscle co-contraction, and thus their contribution to the glenohumeral stability.

Neuromusculoskeletal Modeling-Based Prostheses for Recovery After Spinal Cord Injury

Concurrent stimulation and reinforcement of motor and sensory pathways has been proposed as an effective approach to restoring function after developmental or acquired neurotrauma. This can be achieved by applying multimodal rehabilitation regimens, such as thought-controlled exoskeletons or epidural electrical stimulation to recover motor pattern generation in individuals with spinal cord injury (SCI). However, the human neuromusculoskeletal (NMS) system has often been oversimplified in designing rehabilitative and assistive devices. As a result, the neuromechanics of the muscles is seldom considered when modeling the relationship between electrical stimulation, mechanical assistance from exoskeletons, and final joint movement. A powerful way to enhance current neurorehabilitation is to develop the next generation prostheses incorporating personalized NMS models of patients. This strategy will enable an individual voluntary interfacing with multiple electromechanical rehabilitation devices targeting key afferent and efferent systems for functional improvement. This narrative review discusses how real-time NMS models can be integrated with finite element (FE) of musculoskeletal tissues and interface multiple assistive and robotic devices with individuals with SCI to promote neural restoration. In particular, the utility of NMS models for optimizing muscle stimulation patterns, tracking functional improvement, monitoring safety, and providing augmented feedback during exercise-based rehabilitation are discussed.