Subject-specific trunk segmental masses prediction for musculoskeletal models using artificial neural networks

Accurate determination of body segment parameters is crucial for studying human movement and joint forces using musculoskeletal (MSK) models. However, existing methods for predicting segment mass have limited generalizability and sensitivity to body shapes. With recent advancements in machine learning, this study proposed a novel artificial neural network-based method for computing subject-specific trunk segment mass and center of mass (CoM) using only anthropometric measurements. We first developed, trained, and validated two artificial neural networks that used anthropometric measurements as input to predict body shape (ANN1) and tissue mass (ANN2). Then, we calculated trunk segmental mass for two volunteers using the predicted body shape and tissue mass. The body shape model (ANN1) was tested on 279 subjects, and maximum deviation between the predicted body shape and the original was 28 mm. The tissue mass model (ANN2) was evaluated on 223 subjects, which when compared to ground truth data, had a mean error of less than 0.51% in the head, trunk, legs, and arms. We also compared the two volunteer's trunk segment mass with experimental data and found similar trend and magnitude. Our findings suggested that the proposed method could serve as an effective and convenient tool for predicting trunk mass.

Sensitivity of subject-specific upper body musculoskeletal model predictions to mass scaling methods

Accurate predictions of spinal loads in subject-specific musculoskeletal models require precise body segment parameters, including segment mass and center of mass (CoM) locations. Existing upper body models often assume a constant percentage of total body mass to calculate segmental masses, disregarding inter-individual variability and limiting their predictive capacity. This study evaluated the sensitivity of subject-specific upper body musculoskeletal model predictions to body mass scaling methods. The upper body segmental masses and corresponding CoM of six male subjects with varying body mass indices were computed using two mass scaling methods: the constant-percentage-based (CPB) scaling method, commonly used in AnyBody software; and our recently developed body-shape-based (BSB) method. Subsequently, these values were used by a validated musculoskeletal model to predict the muscle and disc forces in upright and flexed postures. The discrepancies between the results of the two scaling methods were compared across subjects and postures. Maximum deviations in thorax masses reached up to 7.5% of total body weight (TBW) in overweight subjects, while maximum CoM location differences of up to 35 mm were observed in normal weight subjects. The root mean squared errors (RMSE) of the CPB results, calculated with the BSB results as baseline, showed that the muscle and shear forces of the two scaling methods were quite similar (<4.5% of TBW). Though, there were small to moderate differences in compressive forces (6.5-16.0% of TBW). Thus, the compressive forces predicted with CPB method should be used with caution, particularly for overweight and obese subjects.

Baseball Pitching Arm 3-D Inertial Parameter Calculations from Body Composition Imaging and a Novel Overweight Measure for Youth Pitching Arm Kinetics

Many baseball pitching studies have used inverse dynamics to assess throwing arm kinetics as high and repetitive kinetics are thought to be linked to pitching injuries. However, prior studies have not used participant-specific body segment inertial parameters (BSIPs) which are thought to improve analysis of high-acceleration motions and overweight participants. This study's objectives were to 1) calculate participant-specific BSIPs using DXA measures, 2) compare inverse dynamic calculations of kinetics determined by DXA-calculated BSIPs (full DXA-driven inverse dynamics) against kinetics using the standard inverse dynamics approach with scaled BSIPs (scaled inverse dynamics), and 3) examine associations between full DXA-driven kinetics and overweight indices: body mass index (BMI) and segment mass index (SMI). Eighteen participants (10-11 years old) threw 10 fastballs that were recorded for motion analysis. DXA scans were used to calculate participant-specific BSIPs (mass, center of mass, radii of gyration) for each pitching arm segment (upper arm, forearm, hand), BMI, and SMI. The hypotheses were addressed with t-tests and linear regression analyses. The major results were that 1) DXA-calculated BSIPs differed from scaled BSIPs for each pitching arm segment, 2) calculations for shoulder, but not elbow, kinetics differed between the full DXA-driven and scaled inverse dynamics analyses, and 3) full DXA-driven inverse dynamics calculations for shoulder kinetics were more strongly associated with SMI than with BMI. Results suggest that using participant-specific BSIPs and pitching arm SMIs may improve evidence-based injury prevention guidelines for youth pitchers.

Experimental recommendations for estimating lower extremity loading based on joint and activity

Researchers often estimate joint loading using musculoskeletal models to solve the inverse dynamics problem. This approach is powerful because it can be done non-invasively, however, it relies on assumptions and physical measurements that are prone to measurement error. The purpose of this study was to determine the impact of these errors - specifically, segment mass and shear ground reaction force - have on analyzing joint loads during activities of daily living. We performed traditional marker-based motion capture analysis on 8 healthy adults while they completed a battery of exercises on 6 degree of freedom force plates. We then scaled the mass of each segment as well as the shear component of the ground reaction force in 5% increments between 0 and 200% and iteratively performed inverse dynamics calculations, resulting in 1681 mass-shear combinations per activity. We compared the peak joint moments of the ankle, knee, and hip at each mass-shear combination to the 100% mass and 100% shear combination to determine the percent error. We found that the ankle was most resistant to changes in both mass and shear and the knee was resistant to changes in mass while the hip was sensitive to changes in both mass and shear. These results can help guide researchers who are pursuing lower-cost or more convenient data collection setups.

Prediction of human male trunk mass distribution using anthropometric measurements: A feasibility study

The current study proposes a new method to predict the body shape and mass distribution of the trunk (Tl-L5) of a human male using 15 anthropometric measurements acquired at various locations of the body. Trunk cross-sectional images adopted from the Visible Human male project database were segmented into fat, bone, and lean tissue. Assuming that all male subjects have similar cross-sectional composition at a given body height percentile, areas of the segmented cross-sectional images of the Visible Human male along the trunk were scaled to match those of the predicted body shape. The trunk mass distribution of the target subject can then be computed using the density values of fat, bone, and lean tissue. Comparison of the predicted body shape circumference with ground truth values measured using digital and actual measurements yielded maximum mean error of 13.3 mm and 30.3 mm, respectively. The accuracy of the image segmentation was evaluated, and the results showed a high Jaccard index (>0.95). The proposed method was able to predict the trunk mass distribution of two volunteers with a maximum deviation of 384 g at T4 level and a minimum deviation of 12 g at L4 level and the corresponding centers of mass fell within the experimental data at most levels. Thus, our method can be considered as a feasible option to calculate subject-specific trunk mass distribution.

Review of musculoskeletal modelling in a clinical setting: Current use in rehabilitation design, surgical decision making and healthcare interventions

BACKGROUND

Musculoskeletal modelling is a common means by which to non-invasively analyse movement. Such models have largely been used to observe function in both healthy and patient populations. However, utility in a clinical environment is largely unknown. The aim of this review was to explore existing uses of musculoskeletal models as a clinical intervention, or decision-making, tool.

METHODS

A literature search was performed using PubMed and Scopus to find articles published since 2010 and relating to musculoskeletal modelling and joint and muscle forces.

FINDINGS

4662 abstracts were found, of which 39 relevant articles were reviewed. Journal articles were categorised into 5 distinct groups: non-surgical treatment, orthoses assessment, surgical decision making, surgical intervention assessment and rehabilitation regime assessment. All reviewed articles were authored by collaborations between clinicians and engineers/modellers. Current uses included insight into the development of osteoarthritis, identifying candidates for hamstring lengthening surgery, and the assessment of exercise programmes to reduce joint damage.

INTERPRETATION

There is little evidence showing the use of musculoskeletal modelling as a tool for patient care, despite the ability to assess long-term joint loading and muscle overuse during functional activities, as well as clinical decision making to avoid unfavourable treatment outcomes. Continued collaboration between model developers should aim to create clinically-friendly models which can be used with minimal input and experience by healthcare professionals to determine surgical necessity and suitability for rehabilitation regimes, and in the assessment of orthotic devices.

Development of the Biomechanical Technologies for the Modeling of Major Segments of the Human Body: Linking the Past with the Present

Simple Summary

The procedures of body measurement are as old as the inception of the scientific method. The human being has always had the necessity to shape the environment to its own needs, to care for the body and to improve quality of life. Over the centuries, several methods have been developed to measure body size. With the development of measurement sciences, technological tools as well as computational tools have become increasingly precise. This review paper aims to historically review the development of methods for the measurement of body segments from a biomechanical point of view, highlighting the link with the technologies available today.

Abstract

The knowledge of human body proportions and segmental properties of limbs, head and trunk is of fundamental importance in biomechanical research. Given that many methods are employed, it is important to know which ones are currently available, which data on human body masses, lengths, center of mass (COM) location, weights and moment of inertia (MOI) are available and which methods are most suitable for specific research purposes. Graphical, optical, x-ray and derived techniques, MRI, laser, thermography, has been employed for in-vivo measurement, while direct measurements involve cadaveric studies with dissection and various methods of acquiring shape and size of body segments. The present review examines the literature concerning human body segments’ properties for biomechanical purposes starting with a historical examination. It emerges that data obtained in studies on cadaveric specimens are still accurate in comparison to more recent technologies, whilst technological tools currently available are manifolds, each one with proper advantages and disadvantages. Classical studies were focused mainly on white men, while in recent years, the available data on body segments have been extended to children, women, and other races. Additionally, data on special populations (obese, pregnant women) are starting to appear in the scientific literature.

Changes in inertial parameters of the lower limb during the impact phase of dynamic tasks

Mechanical analysis at the whole human body level typically assumes limbs are rigid bodies with fixed inertial parameters, however, as the human body consists mainly of deformable soft tissue, this is not the case. The aim of this study was to investigate changes in the inertial parameters of the lower limb during landing and stamping tasks using high frequency three-dimensional motion analysis. Seven males performed active and passive drop landings from 30 and 45 cm and a stamp onto a force plate. A sixteen-camera 750 Hz Vicon system recorded markers for standard rigid body analysis using inverse kinematics in Visual 3D and 7 × 8 and 7 × 9 marker arrays on the shank and thigh. Frame by frame segment volumes from marker arrays were calculated as a collection of tetrahedra using the Delaunay triangulation method in 3D and further inertial parameters were calculated using the method of Tonon (2004). Distance between the centres of mass (COM) of the rigid and soft tissues changed during impact in a structured manner indicative of a damped oscillation. Group mean amplitudes for COM motion of the soft tissues relative to the rigid body of up to 1.4 cm, and changes of up to 17% in moment of inertia of the soft tissue about the rigid body COM were found. This study has shown that meaningful changes in inertial parameters can be observed and quantified during even moderate impacts. Further examination of the effects these could have on movement dynamics and energetics seems pertinent.

Cervical Spine Injuries: A Whole-Body Musculoskeletal Model for the Analysis of Spinal Loading

Cervical spine trauma from sport or traffic collisions can have devastating consequences for individuals and a high societal cost. The precise mechanisms of such injuries are still unknown as investigation is hampered by the difficulty in experimentally replicating the conditions under which these injuries occur. We harness the benefits of computer simulation to report on the creation and validation of i) a generic musculoskeletal model (MASI) for the analyses of cervical spine loading in healthy subjects, and ii) a population-specific version of the model (Rugby Model), for investigating cervical spine injury mechanisms during rugby activities. The musculoskeletal models were created in OpenSim, and validated against in vivo data of a healthy subject and a rugby player performing neck and upper limb movements. The novel aspects of the Rugby Model comprise i) population-specific inertial properties and muscle parameters representing rugby forward players, and ii) a custom scapula-clavicular joint that allows the application of multiple external loads. We confirm the utility of the developed generic and population-specific models via verification steps and validation of kinematics, joint moments and neuromuscular activations during rugby scrummaging and neck functional movements, which achieve results comparable with in vivo and in vitro data. The Rugby Model was validated and used for the first time to provide insight into anatomical loading and cervical spine injury mechanisms related to rugby, whilst the MASI introduces a new computational tool to allow investigation of spinal injuries arising from other sporting activities, transport, and ergonomic applications. The models used in this study are freely available at simtk.org and allow to integrate in silico analyses with experimental approaches in injury prevention.

Subject-specific body segment parameter estimation using 3D photogrammetry with multiple cameras

Inertial properties of body segments, such as mass, centre of mass or moments of inertia, are important parameters when studying movements of the human body. However, these quantities are not directly measurable. Current approaches include using regression models which have limited accuracy: geometric models with lengthy measuring procedures or acquiring and post-processing MRI scans of participants. We propose a geometric methodology based on 3D photogrammetry using multiple cameras to provide subject-specific body segment parameters while minimizing the interaction time with the participants. A low-cost body scanner was built using multiple cameras and 3D point cloud data generated using structure from motion photogrammetric reconstruction algorithms. The point cloud was manually separated into body segments, and convex hulling applied to each segment to produce the required geometric outlines. The accuracy of the method can be adjusted by choosing the number of subdivisions of the body segments. The body segment parameters of six participants (four male and two female) are presented using the proposed method. The multi-camera photogrammetric approach is expected to be particularly suited for studies including populations for which regression models are not available in literature and where other geometric techniques or MRI scanning are not applicable due to time or ethical constraints.

Biomechanical analysis of three tennis serve types using a markerless system

PURPOSE

The tennis serve is commonly associated with musculoskeletal injury. Advanced players are able to hit multiple serve types with different types of spin. No investigation has characterised the kinematics of all three serve types for the upper extremity and back.

METHODS

Seven NCAA Division I male tennis players performed three successful flat, kick and slice serves. Serves were recorded using an eight camera markerless motion capture system. Laser scanning was utilised to accurately collect body dimensions and data were computed using inverse kinematic methods.

RESULTS

There was no significant difference in maximum back extension angle for the flat, kick or slice serves. The kick serve had a higher force magnitude at the back than the flat and slice as well as larger posteriorly directed shoulder forces. The flat serve had significantly greater maximum shoulder internal rotation velocity versus the slice serve. Force and torque magnitudes at the elbow and wrist were not significantly different between the serves.

CONCLUSIONS

The kick serve places higher physical demands on the back and shoulder while the slice serve demonstrated lower overall kinetic forces. This information may have injury prevention and rehabilitation implications.

Accelerometry-based prediction of movement dynamics for balance monitoring

This paper proposes a 2D functional evaluation tool for estimating subject-specific body segment parameters, which uses a simple motor task (repeated sit-to-stand, rSTS), recorded with one single-axis accelerometer (SAA) per segment and a force plate (FP). After this preliminary estimation, the accelerometer alone is used to make quasi-real-time predictions of ground reaction force (anterior/posterior, F ( X ), and vertical, F ( Z ), components), center of pressure (CoP) and center of mass (CoM), during rSTS and postural oscillation in the sagittal plane. These predicted dynamic variables, as well as those obtained using anthropometric parameters derived from De Leva, were compared to actual FP outputs in terms of root mean-squared errors (RMSEs). Using De Leva's parameters in place of those estimated, RMSEs increase from 12 to 21 N (F ( X )), from 21 to 24 N (F ( Z )), and from 21.1 to 55.6 mm (CoP) in rSTS; similarly, RMSEs increase from 3.1 to 3.3 N (F ( X )) and from 5.5 to 6.6 mm (CoP) in oscillatory trials. A telescopic inverted pendulum model was adopted to analyze the balance control in rSTS using only predicted CoP and CoM. Results suggest that one SAA per segment is sufficient to predict the dynamics of a biomechanical model of any degrees of freedom.