•We developed a complete and coherent musculoskeletal model of the entire human spine and studied the intervertebral disc compression forces.•Intradiscal pressures estimated from predicted ...compressive forces were generally in close agreement with previous measurements of spinal loads both quantitatively and qualitatively.•We found that compressive forces at the trunk discs increased during trunk lateral bending and axial rotation of the trunk.•During trunk flexion, compressive forces increased in the thoracolumbar and lumbar regions and slightly decreased at the middle thoracic discs.•The model predicted increased compression forces in neck flexion, lateral bending, and axial rotation, and decreased forces in neck extension.
Although in vivospinal loads have been previously measured, existing data are limited to certain lumbar and thoracic levels. A detailed investigation of spinal loads would assist with injury prevention and implant design but is unavailable. In this study, we developed a complete and coherent musculoskeletal model of the entire human spine and studied the intervertebral disc compression forces for physiological movements on three anatomical planes. This model incorporates the individual vertebrae at the cervical, thoracic, and lumbar regions, a flexible ribcage, and complete muscle anatomy. Intradiscal pressures were estimated from predicted compressive forces, and these were generally in close agreement with previously measured data. We found that compressive forces at the trunk discs increased during trunk lateral bending and axial rotation of the trunk. During flexion, compressive forces increased in the thoracolumbar and lumbar regions and slightly decreased at the middle thoracic discs. In extension, the forces generally decreased at the thoracolumbar and lumbar discs whereas they slightly increased at the upper and middle thoracic discs. Furthermore, similar to a previous biomechanical model of the cervical spine, our model predicted increased compression forces in neck flexion, lateral bending, and axial rotation, and decreased forces in neck extension.
Electromyography (EMG)-based interfaces are trending toward continuous, simultaneous control with multiple degrees of freedom. Emerging methods range from data-driven approaches to biomechanical ...model-based methods. However, there has been no direct comparison between these two types of continuous EMG-based interfaces. The aim of this study was to compare a musculoskeletal model (MM) with two data-driven approaches, linear regression (LR) and artificial neural network (ANN), for predicting continuous wrist and hand motions for EMG-based interfaces. Six able-bodied subjects and one transradial amputee subject performed (missing) metacarpophalangeal (MCP) and wrist flexion/extension, simultaneously or independently, while four EMG signals were recorded from forearm muscles. To add variation to the EMG signals, the subjects repeated the MCP and wrist motions at various upper extremity postures. For each subject, the EMG signals collected from the neutral posture were used to build the EMG interfaces; the EMG signals collected from all postures were used to evaluate the interfaces. The performance of the interface was quantified by Pearson's correlation coefficient (r) and the normalized root mean square error (NRMSE) between measured and estimated joint angles. The results demonstrated that the MM predicted movements more accurately, with higher r values and lower NRMSE, than either LR or ANN. Similar results were observed in the transradial amputee. Additionally, the variation in r across postures, an indicator of reliability against posture changes, was significantly lower (better) for the MM than for either LR or ANN. Our findings suggest that incorporating musculoskeletal knowledge into EMG-based human-machine interfaces could improve the estimation of continuous, coordinated motion.
Reverse total shoulder arthroplasty (RTSA) accounts for over half of shoulder replacement surgeries. At present, the optimal position of RTSA components is unknown. Previous biomechanical studies ...have investigated the effect of construct placement to quantify mobility, stability and functionality postoperatively. While studies have provided valuable information on construct design and surgical placement, they have not systematically evaluated the importance of scapular morphology on biomechanical outcomes. The aim of this study was to assess the influence of scapular morphology variation on RTSA biomechanics using statistical models, musculoskeletal modeling and predictive simulation. The scapular geometry of a musculoskeletal model was altered across six modes of variation at four levels (±1 and ±3 SD) from a clinically derived statistical shape model. For each model, a standardized virtual surgery was performed to place RTSA components in the same relative position on each model then implemented in 50 predictive simulations of upward and lateral reaching tasks. Results showed morphology affected functional changes in the deltoid moment arms and recruitment for the two tasks. Variation of the anatomy that reduced the efficiency of the deltoids showed increased levels of muscle force production, joint load magnitude and shear. These findings suggest that scapular morphology plays an important role in postoperative biomechanical function of the shoulder with an implanted RTSA. Furthermore a “one‐size‐fits‐all” approach for construct surgical placement may lead to suboptimal patient outcomes across a clinical population. Patient glenoid as well as scapular anatomy may need to be carefully considered when planning RTSA to optimize postoperative success.
The musculoskeletal models have been improved to estimate accurate knee compression force (KCF) and have been used to reveal the causal relationship between KCF and muscle weakness. Previous studies ...have explored how muscle weakness influences the KCF during gait; however, the influence of muscle weakness is possibly larger during activities that require deeper knee flexion (e.g., stair ambulation) than other activities (e.g., gait) because of the small knee contact area of articular surfaces.
To explore how muscle weakness influences the KCF during stair ambulation.
Ten young adults performed stair ascent and descent tasks at a comfortable speed. Based on a previous study, we created muscle weakness models of rectus femoris (RF), vastus muscles (VAS), gluteus medius (Gmed), and gluteus maximus (Gmax), and the medial and lateral KCF (KCFmed and KCFlat) during stair ambulation were calculated.
Similar to the gait, the Gmed weakness increased KCFmed and decreased KCFlat during stair ascent and descent. Whereas, unlike the gait, the Gmax weakness increased KCFmed during stair ascent and the VAS weakness decreased KCFmed and KCFlat during stair ascent and descent. Moreover, the percentage changes in KCF were similar (or large) during stair ambulation compared with those during gait.
Considering the KCF alterations caused by each muscle weakness, the weaknesses in Gmax and Gmed might lead to cartilage loss and pain in the knee, and the VAS weakness might lead to low stability of the knee. The symptom during stair ambulation might help precisely identify the muscle requiring rehabilitation.
•Musculoskeletal models is useful to explore how muscle weakness affects knee compression force.•We explored which muscle weakness influences knee compression force during stair ambulation.•Gluteus medius and maximus weaknesses induced high medial and low lateral knee compression force.•Vastus weakness induced low medial and lateral knee compression force.
We propose a method for automatic segmentation of individual muscles from a clinical CT. The method uses Bayesian convolutional neural networks with the U-Net architecture, using Monte Carlo dropout ...that infers an uncertainty metric in addition to the segmentation label. We evaluated the performance of the proposed method using two data sets: 20 fully annotated CTs of the hip and thigh regions and 18 partially annotated CTs that are publicly available from The Cancer Imaging Archive (TCIA) database. The experiments showed a Dice coefficient (DC) of 0.891±0.016 (mean±std) and an average symmetric surface distance (ASD) of 0.994±0.230 mm over 19 muscles in the set of 20 CTs. These results were statistically significant improvements compared to the state-of-the-art hierarchical multi-atlas method which resulted in 0.845 ± 0.031 DC and 1.556 ± 0.444 mm ASD. We evaluated validity of the uncertainty metric in the multi-class organ segmentation problem and demonstrated a correlation between the pixels with high uncertainty and the segmentation failure. One application of the uncertainty metric in active-learning is demonstrated, and the proposed query pixel selection method considerably reduced the manual annotation cost for expanding the training data set. The proposed method allows an accurate patient-specific analysis of individual muscle shapes in a clinical routine. This would open up various applications including personalization of biomechanical simulation and quantitative evaluation of muscle atrophy.
Under sitting conditions, up-and-down head movements occur during the transition period from awakening to stage 2 non-REM sleep. This paper proposes a model that calculates head movements by giving ...time series of three sleep depths. The proposed model couples a head musculoskeletal model with a muscle activity model. The former is represented by an antagonistic drive system based on muscle contraction characteristics, while the latter varies the magnitude of muscle activity level according to sleep depth. We first measured variations in EEG/EMG around the neck and cervical angle during sleep onset, and based on these characteristics, we constructed two models and developed the proposed model. We then conducted numerical simulations to examine whether the proposed model can represent actual head movements and compared the simulation results with experimental results. The simulation results concluded that the proposed model can represent the experimental results and that the muscle activity waveforms generated in the intermediate stage have experimental characteristics. This model suggests that drowsiness can be estimated from head movements and is expected to be applied to estimate driver's drowsiness.
Although the role of biarticular muscles during squatting and its variations have received considerable attention, the function of these muscles during squatting is not well understood. Closed ...kinetic chain exercises like squats are commonly preferred for knee rehabilitation and strength training for athletes. Squat exercises require both the hip and knee extensors, such as the gluteus maximus, hamstrings, and quadriceps femoris. For the hip extension strategy, the gluteus maximus and hamstrings have an important role, while the hamstrings and quadriceps co-contract at the knee. The same co-contraction occurs at the hip, between the rectus femoris and the hip extensors. These co-contractions do not seem to be effective, in terms of minimum energy expenditure, minimum muscle fatigue, and minimum sense of effort. However, muscular co-contraction is often seen in human movement, and the co-contractions were measured using electromyography (EMG). Although muscle co-contraction is important to modulate joint stability, the co-contraction cannot be predicted in simulations using a musculoskeletal model where the sum of the muscle activations or metabolic energies is minimized. Thus, the activations of those biarticular muscles are clearly underestimated. In this study, EMG were measured during squatting, and interpretations to understand biarticular muscles activations are discussed.
Healthcare workers are highly susceptible to musculoskeletal injury, particularly in their lower back and shoulders. Manual patient transfers are common and can generate physical stresses that ...contribute to these injuries. Few studies have used in vivo musculoskeletal modeling to estimate the effect of slide boards and patient cooperation, and none have used measured hand forces as an input to the models. This laboratory study evaluated manual, one-person bed to wheelchair transfers of a 64 kg simulated patient using an instrumented gait belt that measured hand forces. Thirteen healthcare workers performed transfers with and without a slide board and with up to three levels of vertical assistance (0, 18, and 36% of patient body weight). In vivo lower back forces and resultant shoulder moments were estimated with a thoracolumbar musculoskeletal model using directly measured hand forces and full-body motion capture. Results indicated that slide boards and vertical assistance reduce physical stresses. However, all transfer conditions had trials that exceeded an ergonomic guideline. To provide some guidance on when a transfer can safely be performed manually, a post hoc analysis was performed to estimate the patient mass that can be safely transferred manually under ideal circumstances with only a gait belt. These findings have the potential to guide and credibly educate healthcare workers on when manual transfers are appropriate and when lifts are required. Regardless, mechanical lifts are still recommended in most circumstances to protect caregivers from injury and the patient from falling.
•Manual patient transfers place high physical demands on healthcare workers.•Vertical hand forces during transfers are less than patient bodyweight.•Slide boards and vertical assistance can reduce demands during bed to chair transfers.•Mechanical lifts are needed to properly mitigate injury risk for most patients.
Reinforcement learning (RL) has potential to provide innovative solutions to existing challenges in estimating joint moments in motion analysis, such as kinematic or electromyography (EMG) noise and ...unknown model parameters. Here, we explore feasibility of RL to assist joint moment estimation for biomechanical applications. Forearm and hand kinematics and forearm EMGs from four muscles during free finger and wrist movement were collected from six healthy subjects. Using the proximal policy optimization approach, we trained two types of RL agents that estimated joint moment based on measured kinematics or measured EMGs, respectively. To quantify the performance of trained RL agents, the estimated joint moment was used to drive a forward dynamic model for estimating kinematics, which was then compared with measured kinematics using Pearson correlation coefficient. The results demonstrated that both trained RL agents are feasible to estimate joint moment for wrist and metacarpophalangeal (MCP) joint motion prediction. The correlation coefficients between predicted and measured kinematics, derived from the kinematics-driven agent and subject-specific EMG-driven agents, were 98% ± 1% and 94% ± 3% for the wrist, respectively, and were 95% ± 2% and 84% ± 6% for the metacarpophalangeal joint, respectively. In addition, a biomechanically reasonable joint moment-angle-EMG relationship (i.e., dependence of joint moment on joint angle and EMG) was predicted using only 15 s of collected data. In conclusion, this study illustrates that an RL approach can be an alternative technique to conventional inverse dynamic analysis in human biomechanics study and EMG-driven human-machine interfacing applications.
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