Musculoskeletal Model Research Articles

Comparing on-line continuous movement decoding with joints unconstrained and constrained based on a generic musculoskeletal model.

Human-machine interface (HMI) has been extensively developed and applied in rehabilitation. However, the performance of amputees on continuous movement decoding was significantly decreased compared with that of able-bodied individuals. To explore the impact of the absence of joint movements on the performance of HMI in rehabilitation, a generic musculoskeletal model (MM) was employed in this study to evaluate and compare the performance of subjects completing a series of on-line tasks with the wrist and metacarpophalangeal (MCP) joints unconstrained and constrained. The performance of the generic MM has been demonstrated in previous studies. The electromyography (EMG) signals of four muscles were employed as inputs of the generic MM to realize the continuous movement decoding of wrist and MCP joints. Ten able-bodied subjects were recruited to perform the on-line tasks. The completion time, the number of overshoots, and the path efficiency of the tasks were taken as the indexes to quantify the subjects' performance. The muscle activation associated with the movement was analyzed. Across all tasks and subjects, the average values of the three indexes with the joints unconstrained were 7.7 s, 0.59, and 0.38, respectively, while those with the joints constrained were 17.86 s, 1.47, and 0.22, respectively. The results demonstrated that the subjects performed better with the wrist and MCP joints unconstrained than with those joints constrained in the on-line tasks, suggesting that the absence of joint movements can be a reason of the decreased performance of continuous movement decoding with HMIs. Meanwhile, it is revealed that the different performance on motion behaviors is caused by the absence of joint movements.

Neuro-Musculoskeletal Modeling for Online Estimation of Continuous Wrist Movements from Motor Unit Activities.

Decoding movement intentions from motor unit (MU) activities remains an ongoing challenge, which restricts our comprehension of the intricate transition mechanism from microscopic neural drive to macroscopic movements. This study presents an innovative neuro-musculoskeletal (NMS) model driven by MU activities for online estimation of continuous wrist movements. The proposed model employs a physiological and comprehensive utilization of MU firings and waveforms, thus facilitating the localization of MUs to muscle-tendon units (MTU) as well as the computation of MU-specific neural excitation. Subsequently, the MU-specific neural excitation was integrated to form the MTU-specific neural excitation, which were then inputted into a musculoskeletal model to accomplish the joint angle estimation. To assess the effectiveness of this model, high-density surface electromyography and angular data were collected from the forearms of eight subjects during their performance of wrist flexion-extension task. Two pieces of 8 × 8 electrode arrays and a motion capture system were employed for data acquisition. Following offline model calibration with a global optimization algorithm, online angle estimation results demonstrated a significant superiority of the proposed model over the state-of-the-art NMS models (p < 0.05), yielding the lowest normalized root mean square error (0.10 ± 0.02) and the highest determination coefficient (0.87 ± 0.06). This study provides a novel idea for the decoding of joint movements from MU activities. The research findings hold the potential to advance the development of NMS models towards the control of multiple degrees of freedom, with promising applications in the fields of motor control, biomechanics, and neuro-rehabilitation engineering.

Multibody dynamics-based musculoskeletal modeling for gait analysis: a systematic review.

Beyond qualitative assessment, gait analysis involves the quantitative evaluation of various parameters such as joint kinematics, spatiotemporal metrics, external forces, and muscle activation patterns and forces. Utilizing multibody dynamics-based musculoskeletal (MSK) modeling provides a time and cost-effective non-invasive tool for the prediction of internal joint and muscle forces. Recent advancements in the development of biofidelic MSK models have facilitated their integration into clinical decision-making processes, including quantitative diagnostics, functional assessment of prosthesis and implants, and devising data-driven gait rehabilitation protocols. Through an extensive search and meta-analysis of over 116 studies, this PRISMA-based systematic review provides a comprehensive overview of different existing multibody MSK modeling platforms, including generic templates, methods for personalization to individual subjects, and the solutions used to address statically indeterminate problems. Additionally, it summarizes post-processing techniques and the practical applications of MSK modeling tools. In the field of biomechanics, MSK modeling provides an indispensable tool for simulating and understanding human movement dynamics. However, limitations which remain elusive include the absence of MSK modeling templates based on female anatomy underscores the need for further advancements in this area.

A Machine Learning Approach for Predicting Pedaling Force Profile in Cycling

Accurate measurement of pedaling kinetics and kinematics is vital for optimizing rehabilitation, exercise training, and understanding musculoskeletal biomechanics. Pedal reaction force, the main external force in cycling, is essential for musculoskeletal modeling and closely correlates with lower-limb muscle activity and joint reaction forces. However, sensor instrumentation like 3-axis pedal force sensors is costly and requires extensive postprocessing. Recent advancements in machine learning (ML), particularly neural network (NN) models, provide promising solutions for kinetic analyses. In this study, an NN model was developed to predict radial and mediolateral forces, providing a low-cost solution to study pedaling biomechanics with stationary cycling ergometers. Fifteen healthy individuals performed a 2 min pedaling task at two different self-selected (58 ± 5 RPM) and higher (72 ± 7 RPM) cadences. Pedal forces were recorded using a 3-axis force system. The dataset included pedal force, crank angle, cadence, power, and participants’ weight and height. The NN model achieved an inter-subject normalized root mean square error (nRMSE) of 0.15 ± 0.02 and 0.26 ± 0.05 for radial and mediolateral forces at high cadence, respectively, and 0.20 ± 0.04 and 0.22 ± 0.04 at self-selected cadence. The NN model’s low computational time suits real-time pedal force predictions, matching the accuracy of previous ML algorithms for estimating ground reaction forces in gait.

Altering prosthetic alignment does not affect hip and low back joint loading during sit-to-stand in people with a transtibial amputation

People with a transtibial amputation (TTA) have greater prevalence of low back and hip joint pain compared to the general population. Altered movement, loading patterns, and neuromuscular activation during daily tasks like sit-to-stand likely contribute to these high rates of pain. In addition, muscle activation, ground reaction forces, and trunk range of motion can be affected by prosthetic alignment during sit-to-stand. However, it is unclear how prosthetic alignment affects joint contact forces during this task. The purpose of this study was to investigate the effect of prosthetic alignment on hip and low-back joint loading in people with TTA during sit-to-stand. Kinematics, ground reaction forces, and muscle activity data were collected from 10 people with TTA and 10 age- and sex- matched individuals without limb loss during five self-paced sit-to-stand trials. Participants with TTA completed the sit-to-stand task with their prescribed alignment and six altered alignment conditions (±10 mm anterior/posterior, medial/lateral, and ± 20 mm short/tall). A musculoskeletal model was used to calculate hip and L4-L5 joint loading. There were no differences in hip or L4-L5 joint loading between alignments. Participants with TTA had a greater peak hip joint contact force on the intact side hip compared to the amputated side hip across all alignments. Participants with TTA had greater L4-L5 joint contact force compared to those without amputation. While prosthetic alignment did not affect joint loading during sit-to-stand, future work on additional dynamic tasks is needed to better understand the potential role of prosthetic alignment on joint loading.

Assessment of mechanical variables best describing bone remodelling responses based on their correlation with bone density

Density distribution in bones can be estimated using bone remodelling models (BRM) and applying daily normal loads to assess the stress/strain state to which the bone is subjected. These models locally relate a certain mechanical stimulus, derived from the stress/strain state, directly to bone density or to its variation over time. The background of this idea is Frost’s Mechanostat Theory, which states that overloading states tend to increase bone density and disuse states tend to decrease it. Different variables have been proposed in the literature to measure the mechanical stimulus. Strain energy density (SED) and stresses have been commonly used as mechanical stimuli, but to date their use has not been justified with convincing arguments. In this paper we have selected several variables derived from stress and strain tensors and correlated them with the distribution of bone density in the femur of 13 elderly women to conclude which would be most appropriate for use as a mechanical stimulus in a BRM. We have performed this correlation analysis for six different activities: walking normally, fast walking, stair ascent, stair descent, rising from and sitting on a chair, and jumping in place. Musculoskeletal models were used to estimate joint reaction and muscle forces of each individual for each activity. These were applied to the corresponding finite element model of the femur to obtain stress and strain tensors at each point. The variables proposed as mechanical stimulus and derived from these tensors were correlated to the actual density obtained for each individual from CT-scans. Our results show that stress variables are the best correlated with density. In contrast, the correlations of SED are very weak, so it is not a good candidate for mechanical stimulus. Strains are also weakly correlated to density, but in this case because their distribution across the femur is rather uniform. This is in agreement with the Mechanostat Theory which states that bone reacts to load changes by changing its stiffness so to keep strains in a certain interval. Consequently, a plausible choice for a remodelling criterion could be keeping that strain uniformity.

An open-source framework for the generation of OpenSim models with personalised knee joint geometries for the estimation of articular contact mechanics

Musculoskeletal modelling pipelines typically use generic models scaled to individual’s anthropometry. The ability to represent variations in bone or joint geometry and alignment is highly limited. This may have a large effect, particularly when modelling contact between articular surfaces such as for the knee where articular contact mechanics are used to determine joint kinematics and the resulting cartilage contact pressures and locations. Here we describe a developed open-source framework for the personalisation of such models and compare dynamic simulation outputs. The framework involves three main steps: (1) positions personalised geometries from magnetic resonance imaging and replaces generic bone and contact geometries. (2) Repositions muscle and ligament attachments and via points and optimisation of wrapping surfaces to ensure physiological lengthening behaviour. Finally, (3) muscle and ligament properties are calibrated to ensure physiological behaviour. Following model creation, dynamic simulations from a single participant and gait trial were compared. Small changes in knee adduction/abduction and rotation angles were observed between models. Joint moment differences however were present in not only the knee but also hip and ankle joints. These differences resulted in changes in both the magnitude and location of knee joint contact pressure. The framework developed is automated and requires only minimal user interaction and is built using open-source software packages which can be freely downloaded and installed. The adoption of such personalised modelling approaches facilitates patient specific modelling and may provide more detailed information regarding disease progression, patient stratification and facilitate personalised rehabilitation and treatment planning.

Estimation of joint moments during ball throwing in simulated altered gravity conditions using a musculoskeletal modelling approach: preliminary results

Estimation of joint moments during ball throwing in simulated altered gravity conditions using a musculoskeletal modelling approach: preliminary results

Personalizing the shoulder rhythm in a computational upper body model improves kinematic tracking in high range-of-motion arm movements

Musculoskeletal models of the shoulder are needed to understand the mechanics of overhead motions. Existing models implementing the shoulder rhythm are generic and might not accurately represent an individual’s scapular kinematics. We introduce a method to personalize the shoulder rhythm of a computational model of the upper body that defines the orientations of the clavicle and scapula based on glenohumeral joint angles. During five static calibration poses, we palpate and measure the orientation of the scapula. We explore the importance of representing shoulder elevation by introducing clavicle elevation as a degree of freedom that is independent of the glenohumeral angles. For ten subjects, we record the five calibration poses, ten additional static poses, and dynamic arm raises covering the participants’ full range of motion in each body plane using optical motion capture. We examine the data using a dynamically-constrained inverse kinematics analysis. Shoulder rhythm personalization, independent clavicle elevation, and both in combination reduce the average upper body marker tracking error compared to the generic model in the static poses (26 mm to 17–20 mm) and in the dynamic trials (22 mm to 14–17 mm). Only personalization reduces the average scapula marker error (51 mm to 36–38 mm) and scapula axis-angle error (15° to 10°) compared with the palpated ground truth measurements in the static poses, and in the dynamic trials at instances that best match the static poses (53 mm to 37–40 mm, 15° to 9°). Our results show that personalizing upper body models improves kinematic tracking. We provide our experimental data, model, and methods to allow researchers to reproduce and build upon our results.

Firefighter helmets and cervical intervertebral Kinematics: An OpenSim-Based biomechanical study

The assessment of cervical intervertebral kinematics can serve as the basis for understanding any degenerative changes in the cervical spine due to the prolonged wear of a heavyweight, imbalanced firefighting helmet. Therefore, this study aimed to analyze cervical intervertebral kinematics using the OpenSim musculoskeletal modeling platform in order to provide much-needed insights into how the inertial properties of firefighter helmets affect cervical spinal mobility. A total of 36 firefighters (18 males and 18 females) were recruited to perform static and dynamic neck flexion, extension, and left and right lateral bending tasks for three conditions: 1) no-helmet, 2) US-style helmet with a comparatively superior center of mass (COM), and 3) European-style helmet with relatively higher mass but an inferior COM. Three custom-made OpenSim head-neck models were created to calculate cervical intervertebral kinematics for each helmet condition. Results showed that helmet use significantly (p < 0.001) affects neck and cervical spinal kinematics. Despite its lighter weight, the superior COM placement in the US-style helmet caused more pronounced angular changes and higher velocity of peak flexion and extension angles compared to the European-style helmet across all cervical joints. Moreover, results revealed discrepancies between OpenSim-derived neck and cervical range-of-motion and those reported in previous in-vivo studies. In conclusion, the present study underscores the importance of designing firefighter helmets with a lower profile (less superior COM) to enhance neck range of motion and minimize potential neck injuries.

Enhancing postural balance assessment through neural network-based lower-limb muscle strength evaluation with reduced markers

Aiming to simplify the data acquisition process for balance diagnosis and focused on muscle, a direct factor affecting balance, to assess and judge postural stability. Utilizing a publicly available kinematic dataset, the research retained 3D coordinates and mechanical data for 8 markers on the lower limbs. By integrating this data with the musculoskeletal model in OpenSim, inverse kinematic calculations were performed to derive muscle forces. These forces, alongside the coordinates, were split into an 8:2 training and test set ratio. A neural network was then developed to predict muscle forces using normalized coordinate data from the training set as input, with corresponding muscle force data as training labels. The model’s accuracy was confirmed on the test set, achieving coefficients of determination ( R 2 ) above 0.99 for 276 muscle forces. Furthermore, the Force Maximum Percentage Difference (FMPD) was introduced as a novel criterion to evaluate and visualize lower limb balance, revealing significant discrepancies between the patient and control groups. This study successfully demonstrates that the neural network model can precisely predict lower limb muscle forces using reduced markers and introduces FMPD as an effective tool for assessing limb balance, providing a robust framework for future diagnostic and rehabilitative applications.

Image-Based Musculoskeletal Models to Accurately Reproduce a Maximum Voluntary Isometric Contraction Test In Silico

Image-Based Musculoskeletal Models to Accurately Reproduce a Maximum Voluntary Isometric Contraction Test In Silico

Modeling Muscle Activities of Squat Motion using OpenSIM

Human beings need to be able to monitor their muscle health. Muscle health can be assessed by directly measuring its signal using an electromyography sensor or, recently, from a musculoskeletal modeling software such as OpenSIM. OpenSIM is an open-source platform for modeling, simulating, and analyzing the neuromusculoskeletal system. This work aims to simulate muscle forces during squat movement using OpenSIM simulation and validate the results using electromyography sensors attached to the body. The squat movement data generated from the optical motion capture system and the force plate will be used as inputs for the OpenSIM software. OpenSIM can provide output, such as a graph of the working muscle forces. That graph will be compared and analyzed with a surface electromyography sensor results graph. Based on the analysis results, the chart of working muscle forces generated by the OpenSIM software has shown a similar trend to the graph of muscle activity resulting from surface electromyography sensor reading. Therefore, the simulation results from the OpenSIM software were validated, and it was implied that the data collection process and the modeling were done correctly.

The effect of a soft active back support exosuit on trunk motion and thoracolumbar spine loading during squat and stoop lifts

Back support exosuits aim to reduce tissue demands and thereby risk of injury and pain. However, biomechanical analyses of soft active exosuit designs have been limited. The objective of this study was to evaluate the effect of a soft active back support exosuit on trunk motion and thoracolumbar spine loading in participants performing stoop and squat lifts of 6 and 10 kg crates, using participant-specific musculoskeletal models. The exosuit did not change overall trunk motion but affected lumbo-pelvic motion slightly, and reduced peak compressive and shear vertebral loads at some levels, although shear increased slightly at others. This study indicates that soft active exosuits have limited kinematic effects during lifting, and can reduce spinal loading depending on the vertebral level. These results support the hypothesis that a soft exosuit can assist without limiting trunk movement or negatively impacting skeletal loading and have implications for future design and ergonomic intervention efforts.

Assessing the Reliability of OpenCap and OpenSim as Open Source Softwares for Biomechanical Analysis in Neurological Rehabilitation: A Case Study

The neurological rehabilitation focuses on enhancing functional recovery and improving the quality of life for people who have experienced injuries or diseases affecting the central or peripheral nervous system. This functional recovery includes a follow up of the kinematics of the patients limbs. The use of a open source software such as OpenSim, has been previously proposed as a tool for kinematic analysis, this software allows for highly specialized 3D musculoskeletal modeling, facilitates kinematic analysis and the assessment of force and angles in the lower and upper limbs of the human body. In this context, the propose of this research was to test the reliability of OpenSim for kinematic analysis during neurological rehabilitation. For this goal, the Motricity Index test was done by a group of three healthy participants, this values were used for comparison to the stroke patient who is currently undergoing neurological rehabilitation process. The results demonstrates all the limitations in the range of motion of the patient in comparison to the healthy group due his motor issues, such as muscle spasticity and weakness. This research shows the advantages and limitations of this software and its application in neurological rehabilitation. The goal is to contribute to the development of effective and personalized therapeutic strategies to improve the recovery process for these patients.

Muscle recruitment during gait in individuals with unilateral transfemoral amputation due to trauma compared to able-bodied controls.

Individuals with transfemoral lower limb amputations walk with adapted gait. These kinetic and kinematic compensatory strategies will manifest as differences in muscle recruitment patterns. It is important to characterize these differences to understand the reduced endurance, reduced functionality, and progression of co-morbidities in this population. This study aims to characterize muscle recruitment during gait of highly functional individuals with traumatic transfemoral amputations donning state-of-the-art prosthetics compared to able-bodied controls. Inverse dynamic and static optimisation methods of musculoskeletal modelling were used to quantify muscle forces of the residual and intact limb over a gait cycle for 11 individuals with traumatic transfemoral amputation and for 11 able-bodied controls. Estimates of peak muscle activation and impulse were calculated to assess contraction intensity and energy expenditure. The generalized estimation equation method was used to compare the maximum values of force, peak activation, and impulse of the major muscles. The force exhibited by the residual limb's iliacus, psoas major, adductor longus, tensor fasciae latae and pectineus is significantly higher than the forces in these muscles of the intact contralateral limb group and the able-bodied control group (p < 0.001). These muscles appear to be recruited for their flexor moment arm, indicative of the increased demand due to the loss of the plantar flexors. The major hip extensors are recruited to a lesser degree in the residual limb group compared to the intact limb group (p < 0.001). The plantar flexors of the intact limb appear to compensate for the amputated limb with significantly higher forces compared to the able-bodied controls (p = 0.01). Significant differences found in impulse and peak activation consisted of higher values for the limbs (residual and/or intact) of individuals with transfemoral lower limb amputations compared to the able-bodied controls, demonstrating an elevated cost of gait. This study highlights asymmetry in hip muscle recruitment between the residual and the intact limb of individuals with transfemoral lower limb amputations. Overall elevated impulse and peak activation in the limbs of individuals with transfemoral amputation, compared to able-bodied controls, may manifest in the reduced walking endurance of this population. This demand should be minimised in rehabilitation protocols.

Validation of full-length radiograph based musculoskeletal modeling method to estimate medial and lateral knee contact forces

BackgroundAnatomical parameters of the pelvis, femur, and tibia derived from the full-length radiograph can be used to create a more accurate musculoskeletal model compared to marker-based linear scaling method. However, whether this model leads to more accurate estimations of medial knee contact force (MCF) and lateral knee contact force (LCF) than marker-based linear scaling method is still unknown. Research questionThis main purpose of this study was to determine whether musculoskeletal model generated from full-length radiograph improves the estimations of MCF and LCF. MethodsAn open-source dataset including marker trajectories, ground reaction forces, in vivo knee contact forces, and full-length radiograph was used to evaluate the accuracy of full-length radiograph musculoskeletal modeling method. Subject-specific musculoskeletal models were created using anatomical parameters derived from the full-length radiograph or marker-based linear scaling methods. MCF and LCF were estimated using musculoskeletal simulations of normal walking trails. The accuracy of modeling methods was determined by comparing the estimated and in vivo measured MCF and LCF. ResultsCompared to the marker-based linear scaling approach, the full-length radiograph musculoskeletal modeling method exhibited decreases of 38.3 % and 41.3 % in root mean square error for MCF and LCF respectively, as well as reductions of 50.0 % and 49.3 % in mean peak errors for MCF and LCF respectively. SignificanceThe full-length radiograph musculoskeletal modeling method provides a more accurate way to estimate MCF and LCF compared to the traditional maker-based linear scaling approach, which may contribute to understand the initiation, progression, and treatment of OA.

Kinematic–kinetic compliant acetabular cup positioning based on preoperative motion tracking and musculoskeletal modeling for total hip arthroplasty

The invention of the surgical robot enabled accurate component implantation during total hip arthroplasty (THA). However, a preoperative surgical planning methodology is still lacking to determine the acetabular cup alignment considering the patient-specific hip functions during daily activities such as walking. To simultaneously avoid implant edgeloading and impingement, this study established a kinematic–kinetic compliant (KKC) acetabular cup positioning method based on preoperative gait kinematics measurement and musculoskeletal modeling. Computed tomography images around the hip joint and their biomechanical data during gait, including motion tracking and foot–ground reaction forces, were collected. Using the reconstructed pelvic and femur geometries, the patient-specific hip muscle insertions were located in the lower limb musculoskeletal model via point cloud registration. The designed cup orientation has to be within the patient-specific safe zone to prevent implant impingement, and the optimized value selected based on the time-dependent hip joint reaction force to minimize the risk of edgeloading. As a validation of the proposed musculoskeletal model, the predicted lower limb muscle activations for seven patients were correlated with their surface electromyographic measurements, and the computed hip contact force was also in quantitative agreement with data from the literature. However, the designed cup orientations were not always within the well-known Lewinnek safe zone, highlighting the importance of KKC surgical planning based on patient-specific biomechanical evaluations.

Biomechanical Gait Analysis Using a Smartphone-Based Motion Capture System (OpenCap) in Patients with Neurological Disorders.

This study evaluates the utility of OpenCap (v0.3), a smartphone-based motion capture system, for performing gait analysis in patients with neurological disorders. We compared kinematic and kinetic gait parameters between 10 healthy controls and 10 patients with neurological conditions, including stroke, Parkinson's disease, and cerebral palsy. OpenCap captured 3D movement dynamics using two smartphones, with data processed through musculoskeletal modeling. The key findings indicate that the patient group exhibited significantly slower gait speeds (0.67 m/s vs. 1.10 m/s, p = 0.002), shorter stride lengths (0.81 m vs. 1.29 m, p = 0.001), and greater step length asymmetry (107.43% vs. 91.23%, p = 0.023) compared to the controls. Joint kinematic analysis revealed increased variability in pelvic tilt, hip flexion, knee extension, and ankle dorsiflexion throughout the gait cycle in patients, indicating impaired motor control and compensatory strategies. These results indicate that OpenCap can effectively identify significant gait differences, which may serve as valuable biomarkers for neurological disorders, thereby enhancing its utility in clinical settings where traditional motion capture systems are impractical. OpenCap has the potential to improve access to biomechanical assessments, thereby enabling better monitoring of gait abnormalities and informing therapeutic interventions for individuals with neurological disorders.

Musculoskeletal models determine the effect of a soft active exosuit on muscle activations and forces during lifting and lowering tasks

Exosuits have the potential to mitigate musculoskeletal stress and prevent back injuries during industrial tasks. This study aimed to 1) validate the implementation of a soft active exosuit into a musculoskeletal model of the spine by comparing model predicted muscle activations versus corresponding surface EMG measurements, and 2) evaluate the effect of the exosuit on peak back and hip muscle forces. Fourteen healthy participants performed squat and stoop lift and lower tasks with boxes of 6 and 10 kg, with and without wearing a 2.7 kg soft active exosuit. Participant-specific musculoskeletal models, which included the exosuit, were created in OpenSim. Model validation focused on the back and hip extensors, where temporal agreement between EMG and model estimated muscle activity was generally strong to excellent (average cross-correlation coefficients ranging from 0.84 to 0.98). Root mean square errors of muscle activity (0.05–0.10) were similar with and without the exosuit, and compared well to prior model validation studies without the exosuit (average root mean square errors ranging from 0.05 to 0.19). In terms of performance, the exosuit reduced the estimated peak erector spinae forces during lifting and lowering phases across all lifting tasks but reduced peak hip extensor muscles forces only in a squat lift task of 10 kg. These reductions in total peak muscle forces were approximately 1.7–4.2 times greater than the corresponding exosuit assistance force, which were 146 ± 19 N and 102 ± 14 N at the times of peak erector spinae forces in lifting and lowering, respectively. Overall, the results support the hypothesis that exosuits reduce soft tissue loading, and thereby potentially reduce fatigue and injury risk during manual materials handling tasks. Incorporating exosuits into musculoskeletal models is a valid approach to understand the impact of exosuit assistance on muscle activity and forces.