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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 references 65

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      The Use of Surface Electromyography in Biomechanics

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        Muscle and tendon: properties, models, scaling, and application to biomechanics and motor control.

         Laura E Zajac (1988)
        Skeletal muscles transform neural control signals into forces that act upon the body segments to effect a coordinated motor task. This transformation is complex, not only because the properties of muscles are complex, but because the tendon affects the transmission of muscle force to the skeleton. This review focuses on how to synthesize basic properties of muscle and tendon to construct models applicable to studies of coordination. After a review of the properties of muscle and tendon, their integrated ability to generate force statically and dynamically is studied by formulating a generic model of the "musculotendon actuator", which has only one parameter, the ratio of tendon length at rest to muscle fiber length at rest. To illustrate the utility of the model, it is analyzed to show how this one parameter specifies whether excitation-contraction or musculotendon contraction is the rate-limiting process of force generation, whether elastic energy is stored in tendon or muscle, and whether hip- and knee-extensor actuators function as springs or dashpots during walking.
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          An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo.

           D Lloyd,  Thor Besier (2003)
          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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            Author and article information

            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
            Journal
            Sensors (Basel)
            Sensors (Basel)
            sensors
            Sensors (Basel, Switzerland)
            MDPI
            1424-8220
            14 June 2017
            June 2017
            : 17
            : 6
            28613265
            5492481
            10.3390/s17061389
            sensors-17-01389
            © 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/).

            Categories
            Article

            Biomedical engineering

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

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