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      A Novel Approach to Measuring Muscle Mechanics in Vehicle Collision Conditions

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          Abstract

          The aim of the study was to evaluate a novel approach to measuring neck muscle load and activity in vehicle collision conditions. A series of sled tests were performed on 10 healthy volunteers at three severity levels to simulate low-severity frontal impacts. Electrical activity—electromyography (EMG)—and muscle mechanical tension was measured bilaterally on the upper trapezius. A novel mechanical contraction (MC) sensor was used to measure the tension on the muscle surface. The neck extensor loads were estimated based on the inverse dynamics approach. The results showed strong linear correlation (Pearson’s coefficient r ¯ P = 0.821) between the estimated neck muscle load and the muscle tension measured with the MC sensor. The peak of the estimated neck muscle force delayed 0.2 ± 30.6 ms on average vs. the peak MC sensor signal compared to the average delay of 61.8 ± 37.4 ms vs. the peak EMG signal. The observed differences in EMG and MC sensor collected signals indicate that the MC sensor offers an additional insight into the analysis of the neck muscle load and activity in impact conditions. This approach enables a more detailed assessment of the muscle-tendon complex load of a vehicle occupant in pre-impact and impact conditions.

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          Most cited references60

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          An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo.

          This paper examined if an electromyography (EMG) driven musculoskeletal model of the human knee could be used to predict knee moments, calculated using inverse dynamics, across a varied range of dynamic contractile conditions. Muscle-tendon lengths and moment arms of 13 muscles crossing the knee joint were determined from joint kinematics using a three-dimensional anatomical model of the lower limb. Muscle activation was determined using a second-order discrete non-linear model using rectified and low-pass filtered EMG as input. A modified Hill-type muscle model was used to calculate individual muscle forces using activation and muscle tendon lengths as inputs. The model was calibrated to six individuals by altering a set of physiologically based parameters using mathematical optimisation to match the net flexion/extension (FE) muscle moment with those measured by inverse dynamics. The model was calibrated for each subject using 5 different tasks, including passive and active FE in an isokinetic dynamometer, running, and cutting manoeuvres recorded using three-dimensional motion analysis. Once calibrated, the model was used to predict the FE moments, estimated via inverse dynamics, from over 200 isokinetic dynamometer, running and sidestepping tasks. The inverse dynamics joint moments were predicted with an average R(2) of 0.91 and mean residual error of approximately 12 Nm. A re-calibration of only the EMG-to-activation parameters revealed FE moments prediction across weeks of similar accuracy. Changing the muscle model to one that is more physiologically correct produced better predictions. The modelling method presented represents a good way to estimate in vivo muscle forces during movement tasks.
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            Neuromusculoskeletal modeling: estimation of muscle forces and joint moments and movements from measurements of neural command.

            This paper provides an overview of forward dynamic neuromusculoskeletal modeling. The aim of such models is to estimate or predict muscle forces, joint moments, and/or joint kinematics from neural signals. This is a four-step process. In the first step, muscle activation dynamics govern the transformation from the neural signal to a measure of muscle activation-a time varying parameter between 0 and 1. In the second step, muscle contraction dynamics characterize how muscle activations are transformed into muscle forces. The third step requires a model of the musculoskeletal geometry to transform muscle forces to joint moments. Finally, the equations of motion allow joint moments to be transformed into joint movements. Each step involves complex nonlinear relationships. The focus of this paper is on the details involved in the first two steps, since these are the most challenging to the biomechanician. The global process is then explained through applications to the study of predicting isometric elbow moments and dynamic knee kinetics.
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              Model-based estimation of muscle forces exerted during movements.

              Estimation of individual muscle forces during human movement can provide insight into neural control and tissue loading and can thus contribute to improved diagnosis and management of both neurological and orthopaedic conditions. Direct measurement of muscle forces is generally not feasible in a clinical setting, and non-invasive methods based on musculoskeletal modeling should therefore be considered. The current state of the art in clinical movement analysis is that resultant joint torques can be reliably estimated from motion data and external forces (inverse dynamic analysis). Static optimization methods to transform joint torques into estimates of individual muscle forces using musculoskeletal models, have been known for several decades. To date however, none of these methods have been successfully translated into clinical practice. The main obstacles are the lack of studies reporting successful validation of muscle force estimates, and the lack of user-friendly and efficient computer software. Recent advances in forward dynamics methods have opened up new opportunities. Forward dynamic optimization can be performed such that solutions are less dependent on measured kinematics and ground reaction forces, and are consistent with additional knowledge, such as the force-length-velocity-activation relationships of the muscles, and with observed electromyography signals during movement. We conclude that clinical applications of current research should be encouraged, supported by further development of computational tools and research into new algorithms for muscle force estimation and their validation.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                14 June 2017
                June 2017
                : 17
                : 6
                : 1389
                Affiliations
                [1 ]Faculty of Mechanical Engineering, University of Ljubljana, Aškerčeva cesta 6, 1000 Ljubljana, Slovenia; ana.trajkovski@ 123456fs.uni-lj.si
                [2 ]TMG-BMC d.o.o., Štihova ulica 24, 1000 Ljubljana, Slovenia; srdjand@ 123456tmg.si
                [3 ]Faculty of Medicine, University of Ljubljana, Korytkova ulica 2, 1000 Ljubljana, Slovenia; marjana.hribernik@ 123456mf.uni-lj.si
                Author notes
                [* ]Correspondence: simon.krasna@ 123456fs.uni-lj.si ; Tel.: +386-1-4771-186
                Article
                sensors-17-01389
                10.3390/s17061389
                5492481
                28613265
                ced241d6-c0ee-4bdd-9235-f29b94a83fea
                © 2017 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 20 April 2017
                : 09 June 2017
                Categories
                Article

                Biomedical engineering
                biomechanics,vehicle occupant,impact,in vivo,active muscle
                Biomedical engineering
                biomechanics, vehicle occupant, impact, in vivo, active muscle

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