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[Display omitted] •Model muscle strength personalized using maximum voluntary isometric contractions.•Model segmental mass personalized using dual-energy x-ray absorptiometry.•Personalization of biomechanical models affects simulation joint contact forces.•Changes in internal forces due to load carriage varied across the cohort. Military overuse injuries are associated with greater internal loads during load carriage and cause decreased readiness and increased costs. Low physical fitness, including low muscle strength, is a modifiable injury risk factor, making it an important focus for injury prevention. Evaluating potential injury mechanisms from biomechanical modeling and simulation may be useful, particularly for individuals who have unique muscle strengths and body segment mass distributions. However, modeled muscle strength from average adults is likely underestimated for trained, active-duty military personnel, which may influence internal load calculations. Thus, we evaluated the effects of muscle strength and segment mass distribution personalization on joint contact impulses and peak forces, and muscle force impulses from musculoskeletal simulations. Full-body kinematics, ground reaction forces, and electromyography were collected from 16 active-duty participants under two walking conditions: 1) no-pack and 2) pack (posteriorly added load, total 46 kg). These data drove and validated simulations of models with different types of personalization. Modeled segment masses were scaled using measured regional masses from dual-energy x-ray absorptiometry. Modeled muscle strengths were scaled from eight maximum voluntary isometric contractions measured using an instrumented dynamometer including the lumbar, hip, knee, and ankle. Personalized mass-distribution scaling had 6% greater lumbar joint contact impulses in walking compared to not personalized (p = .006). Personalized muscle strength scaling had greater joint contact impulses at the lumbar (42%), hip (25%), and knee (8%) (p < .001), and smaller impulses at the ankle (−4%) (p = .048) compared to not personalized. Results were similar for peak joint contact forces. Model personalization is beneficial to quantify internal loading and evaluate training interventions.

Background Distal radius fractures (DRFs) are common fracture types and elderly patients often struggle to achieve functional recovery, which could be overcome by precise rehabilitation. This study aims to develop an innovative approach for acquiring patient-specific musculoskeletal models to provide guidelines for therapists to tailor rehabilitation plans individually. Method A wearable EMG detector (Myo armband) and a dynamometer (KDG grip strength tester, EH101) were used to collect EMG signals and grip forces from 20 volunteers at 0, 30, 50, 70, and 100 N, which were considered low-level gripping. The collected data was used to train neural networks to predict maximum grip force from low-level grip data only. Based on a novel scaling function, personalized models were scaled from a standard musculoskeletal model and were validated by comparing their results with experiments. Sequentially, the musculoskeletal forces of two volunteers with different muscle strengths (one strong in muscle strength and the other is weak, compared to baseline) were simulated under extension exercises to investigate the impact of individual muscle strengths on rehabilitation outcomes. Results The trained model predicts the maximum grip force by EMG signals well. Based on the scaling function, the corresponding personalized musculoskeletal models can simulate grip forces that align well with experiment observations. The muscle loadings were also scaled proportionally to their scaling coefficients. However, the contact forces are not linear to the scaling coefficients. The healing outcome of weak individuals shows satisfactory improvement while that of strong individuals performs ordinarily. Conclusion This study has successfully developed a convenient approach to detect the maximum grip strength of patients and verified the feasibility of scaling the musculoskeletal models. The non-linear relationship of contract forces to the scaling coefficients indicates the complexity of the musculoskeletal system. The healing outcomes from the case studies suggest that while adequate mechanical stimuli are beneficial, excessive or inappropriate stimuli can impede the healing process.

Neck pain and injuries are growing healthcare burdens with women having a higher incidence rate and poorer treatment outcomes than males. A better understanding of sex differences in neck biomechanics, foundational for more targeted, effective prevention or treatment strategies, calls for more advanced subject-specific musculoskeletal modeling. Current neck musculoskeletal models are based on generic anatomy, lack subject specificity beyond anthropometric scaling, and are unable to accurately reproduce neck strengths exhibited in vivo without arbitrary muscle force scaling factors or residual torque actuators. In this work, subject-specific neck musculoskeletal models of 23 individuals (11 male, 12 female) were constructed by integrating multi-modality imaging and biomechanical measurements. Each model simulated maximal voluntary neck static exertions in three postures: neck flexion in a neutral posture, flexion in a 40° extended posture, and extension in a 40° flexed posture. Quantitative model validation showed close agreement between model-predicted muscle activation and EMG measurement. The models unveiled that (1) males have greater moment arms in one flexor muscle group and five extensor muscle groups, (2) females exhibited higher cervical spinal compression per unit exertion force in the flexed posture, and (3) the variability of compression force was much greater in females in all three exertions but most notably in the extension with a flexed “dropped head” position. These insights illuminated a plausible pathway from sex differences in neck biomechanics to sex disparities in the risk and prevalence of neck pain.

Lower limb external maximal power output capacity is a key physical component of performance in many sports. During squat jump and countermovement jump tests, athletes produce high amounts of mechanical work over a short duration to displace their body mass (i.e. the dimension of mechanical power). Thus, jump height has been frequently used by the sports science and medicine communities as an indicator of the power output produced during the jump and by extension, of maximal power output capacity. However, in this article, we contend that squat jump and countermovement jump height are not systematically good indicators of power output produced during the jump and maximal power output capacity. To support our opinion, we first detail why, theoretically, jump height and maximal power output capacity are not fully related. Specifically, we demonstrate that individual body mass, push-off distance, optimal loading and the force-velocity profile confound the jump height–power relationship. We also discuss the relationship between squat jump or countermovement jump height and maximal power output capacity measured with a force plate based on data reported in the literature, which added to our own experimental evidence. Finally, we discuss the limitations of existing practical solutions (regression-based estimation equations and allometric scaling), and advocate using a valid, reliable and simple field-based procedure to compute individual power output produced during the jump and maximal power output capacity directly from jump height, body mass and push-off distance. The latter may allow researchers and practitioners to reduce bias in their assessment of lower limb mechanical power output by using jump height as an input with a simple yet accurate computation method, and not as the first/only variable of interest.

Cerebral palsy (CP) is a neurological disorder that results in life-long mobility impairments. Musculoskeletal models used to investigate mobility deficits for children with CP often lack subject-specific characteristics such as altered muscle strength, despite a high prevalence of muscle weakness in this population. We hypothesized that incorporating subject-specific strength scaling within musculoskeletal models of children with CP would improve accuracy of muscle excitation predictions in walking simulations. Ten children (13.5 ± 3.3 years; GMFCS level II) with spastic CP participated in a gait analysis session where lower-limb kinematics, ground reaction forces, and bilateral electromyography (EMG) of five lower-limb muscles were collected. Isometric strength was measured for each child using handheld dynamometry. Three musculoskeletal models were generated for each child including a ‘Default’ model with the generic musculoskeletal model’s muscle strength, a ‘Uniform’ model with muscle strength scaled allometrically, and a ‘Custom’ model with muscle strength scaled based on handheld dynamometry strength measures. Muscle-driven gait simulations were generated using each model for each child. Simulation accuracy was evaluated by comparing predicted muscle excitations and measured EMG signals, both in the duration of muscle activity and the root-mean-square difference (RMSD) between signals. Improved agreement with EMG were found in both the ‘Custom’ and ‘Uniform’ models compared to the ‘Default’ model indicated by improvement in RMSD summed across all muscles, as well as RMSD and duration of activity for individual muscles. Incorporating strength scaling into musculoskeletal models can improve the accuracy of walking simulations for children with CP.

PurposeTo compare measurements of muscle strength, size, and neuromuscular function among pre-adolescent and adolescent boys and girls with distinctly different strength capabilities.MethodsFifteen boys (mean age ± confidence interval: 13.0 ± 1.0 years) and 13 girls (12.9 ± 1.1 years) were categorized as low strength (LS, n = 14) or high strength (HS, n = 14) based on isometric maximal voluntary contraction strength of the leg extensors. Height (HT), seated height, and weight (WT) determined maturity offset, while percent body fat and fat-free mass (FFM) were estimated from skinfold measurements. Quadriceps femoris muscle cross-sectional area (CSA) was assessed from ultrasound images. Isometric ramp contractions of the leg extensors were performed while surface electromyographic amplitude (EMGRMS) and mechanomyographic amplitude (MMGRMS) were recorded for the vastus lateralis (VL). Neuromuscular efficiency from the EMG and MMG signals (NMEEMG and NMEMMG, respectively) and log-transformed EMG and MMG vs. torque relationships were also used to examine neuromuscular responses.ResultsHS was 99–117% stronger, 2.3–2.8 years older, 14.0–15.7 cm taller, 20.9–22.3 kg heavier, 2.3–2.4 years more biologically mature, and exhibited 39–43% greater CSA than LS (p ≤ 0.001). HS exhibited 74–81% higher NMEEMG than LS (p ≤ 0.022), while HS girls exhibited the highest NMEMMG (p ≤ 0.045). Even after scaling for HT, WT, CSA, and FFM, strength was still 36–90% greater for HS than LS (p ≤ 0.031). The MMGRMS patterns in the LS group displayed more type I motor unit characteristics.ConclusionsNeuromuscular adaptations likely influence strength increases from pre-adolescence to adolescence, particularly when examining large, force-producing muscles and large strength differences explained by biological maturity, rather than simply age.

Musculoskeletal models and computer simulations enable non-invasive study of muscle function and contact forces. Hand models are useful for understanding the complexities of hand strength, precision movement, and the dexterity required during daily activities. Yet, generic models fail to accurately represent the entire scope of the population, while subject-specific models are labor-intensive to create. The objective of this study was to assess the efficacy of scaled generic models to represent the broad spectrum of strength profiles across the lifespan. We examined one hundred lateral pinch simulations using a generic model of the wrist and thumb anthropometrically scaled to represent the full range of heights reported for four ages across childhood, puberty, older adolescence, and adulthood. We evaluated maximum lateral pinch force produced, muscle control strategies, and the effect of linearly scaling the maximum isometric force. Our simulations demonstrated three main concepts. First, anthropometric scaling could capture age-dependent differences in pinch strength. Second, a generic muscle control strategy is not representative of all populations. Lastly, simulations do not employ optimal fiber length to complete a lateral pinch task. These results demonstrate the potential of anthropometrically-scaled models to study hand strength across the lifespan, while also highlighting that muscle control strategies may adapt as we age. The results also provide insight to the force–length relationship of thumb muscles during lateral pinch. We conclude that anthropometric scaling can accurately represent age characteristics of the population, but subject-specific models are still necessary to represent individuals.

Rapid force generation across submaximal levels has been evaluated with the rate of force development scaling factor (RFD-SF) in different isometric tasks, while such measurement was still not verified in dynamic tasks. Our study was designed to evaluate the feasibility of the RFD-SF in dynamic drop jump (DJ) task (RFD-SFDJ). A total of 55 young athletes performed isometric plantarflexion at different submaximal intensities and 60 DJs (6 different drop heights). For each participant we calculated linearity (r2) and slope in isometric task (RFD-SFPF), eccentric part of DJ (RFD-SFDJ-ECC) and concentric part of DJ (RFD-SFDJ-CON), as well as average jump height (DJH) from each drop height. Our results revealed strong linear force-RFD relationship for isometric plantarflexion (r2 = 0.90 ± 0.06), eccentric (r2 = 0.87 ± 0.09) and concentric phase of DJ (r2 = 0.80 ± 0.18). Significant moderate positive correlations were calculated between RFD-SFPF and RFD-SFDJ-ECC (r = 0.311, p < 0.05) and small negative correlations between RFD-SFDJ-CON and RFD-SF (r = -0.276, p < 0.05). Significant positive moderate correlations were seen only between RFD-SFDJ-ECC and DJH from 10 cm (r = 0.459, p < 0.001) and 15 cm (r = 0.423, p < 0.01). This is the first study to introduce and confirm that RFD-SFDJ can be obtained from the multi-joint tasks (60 jumps) and still provide acceptable reliability and linear relationship. Furthermore, RFD-SFDJ may have greater practical application than RFD-SF assessed under the isometric conditions. This verification of RFD-SFDJ opens opportunities for further research regarding its practical application.

In many swimming and flying animals, propulsion emerges from the interplay of active muscle contraction, passive body elasticity and fluid–body interaction. Changes in the active and passive body properties can influence performance and cost of transport across a broad range of scales; they specifically affect the vortex generation that is crucial for effective swimming at higher Reynolds numbers. Theoretical models that account for both active contraction and passive elasticity are needed to understand how animals tune both their active and passive properties to move efficiently through fluids. This is particularly significant when one considers the phylogenetic constraints on the jellyfish mechanospace, such as the presence of relatively weak muscles that are only one cell layer thick. In this work, we develop an actively deforming model of a jellyfish immersed in a viscous fluid and use numerical simulations to study the role of active muscle contraction, passive body elasticity and fluid forces in the medusan mechanospace. By varying the strength of contraction and the flexibility of the bell margin, we quantify how these active and passive properties affect swimming speed and cost of transport. We find that for fixed bell elasticity, swimming speed increases with the strength of contraction. For fixed force of contractility, swimming speed increases as margin elasticity decreases. Varying the strength of activation in proportion to the elasticity of the bell margin yields similar swimming speeds, with a cost of transport is substantially reduced for more flexible margins. A scaling study reveals that performance declines as the Reynolds number decreases. Circulation analysis of the starting and stopping vortex rings showed that their strengths were dependent on the relative strength of activation with respect to the bell margin flexibility. This work yields a computational framework for developing a quantitative understanding of the roles of active and passive body properties in swimming.

A new methodology was developed to quickly generate whole body models with detailed neck musculoskeletal architecture that are properly scaled in terms of anthropometry and muscle strength. This method was implemented in an anthropometric model generation software that allows users to interactively generate any new male or female musculoskeletal models with adjustment of anthropometric parameters (such as height, weight, neck circumference, and neck length) without the need of subject-specific motion capture or medical images. 50th percentile male and female models were developed based on the 2012 US Army Anthropometric Survey (ANSUR II) database and optimized with a novel bilevel optimization method to have strengths comparable to experimentally measured values in the literature. Other percentile models (ranging from the 1st to 99th percentile) were generated based on anthropometric scaling of the 50th percentile models and compared. The resultant models are reasonably accurate in terms of both musculoskeletal geometry and neck strength, demonstrating the effectiveness of the developed methodology for interactive neck model generation with anthropometric scaling.