skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: A three‐dimensional musculoskeletal model of the pelvis and lower limb of Australopithecus afarensis
Abstract ObjectivesMusculoskeletal modeling is a powerful approach for studying the biomechanics and energetics of locomotion.Australopithecus (A.) afarensisis among the best represented fossil hominins and provides critical information about the evolution of musculoskeletal design and locomotion in the hominin lineage. Here, we develop and evaluate a three‐dimensional (3‐D) musculoskeletal model of the pelvis and lower limb ofA. afarensisfor predicting muscle‐tendon moment arms and moment‐generating capacities across lower limb joint positions encompassing a range of locomotor behaviors. Materials and MethodsA 3‐D musculoskeletal model of an adultA. afarensispelvis and lower limb was developed based primarily on the A.L. 288‐1 partial skeleton. The model includes geometric representations of bones, joints and 35 muscle‐tendon units represented using 43 Hill‐type muscle models. Two muscle parameter datasets were created from human and chimpanzee sources. 3‐D muscle‐tendon moment arms and isometric joint moments were predicted over a wide range of joint positions. ResultsPredicted muscle‐tendon moment arms generally agreed with skeletal metrics, and corresponded with human and chimpanzee models. Human and chimpanzee‐based muscle parameterizations were similar, with some differences in maximum isometric force‐producing capabilities. The model is amenable to size scaling from A.L. 288‐1 to the larger KSD‐VP‐1/1, which subsumes a wide range of size variation inA. afarensis. DiscussionThis model represents an important tool for studying the integrated function of the neuromusculoskeletal systems inA. afarensis. It is similar to current human and chimpanzee models in musculoskeletal detail, and will permit direct, comparative 3‐D simulation studies.  more » « less
Award ID(s):
2018523 2018436
PAR ID:
10458193
Author(s) / Creator(s):
 ;  ;  
Publisher / Repository:
Wiley Blackwell (John Wiley & Sons)
Date Published:
Journal Name:
American Journal of Biological Anthropology
Volume:
183
Issue:
3
ISSN:
2692-7691
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract Cerebrovascular accidents like a stroke can affect the lower limb as well as upper extremity joints (i.e., shoulder, elbow, or wrist) and hinder the ability to produce necessary torque for activities of daily living. In such cases, muscles’ ability to generate forces reduces, thus affecting the joint’s torque production. Understanding how muscles generate forces is a key element to injury detection. Researchers have developed several computational methods to obtain muscle forces and joint torques. Electromyography (EMG) driven modeling is one of the approaches to estimate muscle forces and obtain joint torques from muscle activity measurements. Musculoskeletal models and EMG-driven models require necessary muscle-specific parameters for the calculation. The focus of this study is to investigate the EMG-driven approach along with an upper extremity musculoskeletal model to determine muscle forces of two major muscle groups, biceps brachii and triceps brachii, consisting of seven muscle-tendon units. Estimated muscle forces are used to determine the elbow joint torque. Experimental EMG signals and motion capture data are collected for a healthy subject. The musculoskeletal model is scaled to match the geometric parameters of the subject. Then, the approach calculates muscle forces and joint moment for two tasks: simple elbow flexion extension and triceps kickback. Individual muscle forces and net joint torques for both tasks are estimated. The study also has compared the effect of muscle-tendon parameters (optimal fiber length and tendon slack length) on the estimated results. 
    more » « less
  2. Humans are unique among terrestrial mammals in our manner of walking and running, reflecting 7 to 8 Ma of musculoskeletal evolution since diverging with the genus Pan. One component of this is a shift in our skeletal muscle biology towards a predominance of myosin heavy chain (MyHC) I isoforms (i.e. slow fibers) across our pelvis and lower limbs, which distinguishes us from chimpanzees. Here, new MyHC data from 35 pelvis and hind limb muscles of a Western gorilla (Gorilla gorilla) are presented. These data are combined with a similar chimpanzee dataset to assess the MyHC I content of humans in comparison to African apes (chimpanzees and gorillas) and other terrestrial mammals. The responsiveness of human skeletal muscle to behavioral interventions is also compared to the human-African ape differential. Humans are distinct from African apes and among a small group of terrestrial mammals whose pelvis and lower limb muscle is slow fiber dominant, on average. Behavioral interventions, including immobilization, bed rest, spaceflight and exercise, can induce modest decreases and increases in human MyHC I content (i.e. -9.3% to 2.3%, n = 2033 subjects), but these shifts are much smaller than the mean human-African ape differential (i.e. 31%). Taken together, these results indicate muscle fiber content is likely an evolvable trait under selection in the hominin lineage. As such, we highlight potential targets of selection in the genome (e.g. regions that regulate MyHC content) that may play an important role in hominin skeletal muscle evolution. 
    more » « less
  3. Abstract 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). 
    more » « less
  4. Abstract BackgroundImproving the prediction ability of a human-machine interface (HMI) is critical to accomplish a bio-inspired or model-based control strategy for rehabilitation interventions, which are of increased interest to assist limb function post neurological injuries. A fundamental role of the HMI is to accurately predict human intent by mapping signals from a mechanical sensor or surface electromyography (sEMG) sensor. These sensors are limited to measuring the resulting limb force or movement or the neural signal evoking the force. As the intermediate mapping in the HMI also depends on muscle contractility, a motivation exists to include architectural features of the muscle as surrogates of dynamic muscle movement, thus further improving the HMI’s prediction accuracy. ObjectiveThe purpose of this study is to investigate a non-invasive sEMG and ultrasound (US) imaging-driven Hill-type neuromuscular model (HNM) for net ankle joint plantarflexion moment prediction. We hypothesize that the fusion of signals from sEMG and US imaging results in a more accurate net plantarflexion moment prediction than sole sEMG or US imaging. MethodsTen young non-disabled participants walked on a treadmill at speeds of 0.50, 0.75, 1.00, 1.25, and 1.50 m/s. The proposed HNM consists of two muscle-tendon units. The muscle activation for each unit was calculated as a weighted summation of the normalized sEMG signal and normalized muscle thickness signal from US imaging. The HNM calibration was performed under both single-speed mode and inter-speed mode, and then the calibrated HNM was validated across all walking speeds. ResultsOn average, the normalized moment prediction root mean square error was reduced by 14.58 % ($$p=0.012$$ p = 0.012 ) and 36.79 % ($$p<0.001$$ p < 0.001 ) with the proposed HNM when compared to sEMG-driven and US imaging-driven HNMs, respectively. Also, the calibrated models with data from the inter-speed mode were more robust than those from single-speed modes for the moment prediction. ConclusionsThe proposed sEMG-US imaging-driven HNM can significantly improve the net plantarflexion moment prediction accuracy across multiple walking speeds. The findings imply that the proposed HNM can be potentially used in bio-inspired control strategies for rehabilitative devices due to its superior prediction. 
    more » « less
  5. In evolutionary biomechanics, musculoskeletal computer models of extant and extinct taxa are often used to estimate joint range of motion (ROM) and muscle moment arms (MMAs), two parameters which form the basis of functional inferences. However, relatively few experimental studies have been performed to validate model outputs. Previously, we built a model of the short-beaked echidna ( Tachyglossus aculeatus ) forelimb using a traditional modelling workflow, and in this study we evaluate its behaviour and outputs using experimental data. The echidna is an unusual animal representing an edge-case for model validation: it uses a unique form of sprawling locomotion, and possesses a suite of derived anatomical features, in addition to other features reminiscent of extinct early relatives of mammals. Here we use diffusible iodine-based contrast-enhanced computed tomography (diceCT) alongside digital and traditional dissection to evaluate muscle attachments, modelled muscle paths, and the effects of model alterations on the MMA outputs. We use X-ray Reconstruction of Moving Morphology (XROMM) to compare ex vivo joint ROM to model estimates based on osteological limits predicted via single-axis rotation, and to calculate experimental MMAs from implanted muscles using a novel geometric method. We also add additional levels of model detail, in the form of muscle architecture, to evaluate how muscle torque might alter the inferences made from MMAs alone, as is typical in evolutionary studies. Our study identifies several key findings that can be applied to future models. 1) A light-touch approach to model building can generate reasonably accurate muscle paths, and small alterations in attachment site seem to have minimal effects on model output. 2) Simultaneous movement through multiple degrees of freedom, including rotations and translation at joints, are necessary to ensure full joint ROM is captured; however, single-axis ROM can provide a reasonable approximation of mobility depending on the modelling objectives. 3) Our geometric method of calculating MMAs is consistent with model-predicted MMAs calculated via partial velocity, and is a potentially useful tool for others to create and validate musculoskeletal models. 4) Inclusion of muscle architecture data can change some functional inferences, but in many cases reinforced conclusions based on MMA alone. 
    more » « less