A Human Body Model With Active Muscles for Simulation of Pretensioned Restraints in Autonomous Braking Interventions

Östh, Jonas; Brolin, Karin; Bråse, Dan

doi:10.1080/15389588.2014.931949

Cited by 46 publications

(31 citation statements)

References 20 publications

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“…NELnumber of elements. Muscle data for the upper body and extremities are presented in [4,16]. constant equal to zero; hence, postural feedback control was aiming to maintain the initial posture.…”

Section: Maximum Driver Braking Simulationsmentioning

confidence: 99%

“…The THUMS 1 model has previously been complemented with 348 line muscle elements representing the muscles of the neck, lumbar and abdominal areas [4], and the upper extremities [16]. A Hill-type muscle material is used, in which the maximum isometric stress is 1 MPa for the upper extremity muscles [17] and 0.5 MPa [18] for the other muscles in the model.…”

Section: Musculoskeletal Feedback Control Modelmentioning

confidence: 99%

“…First, REF, a reflexive baseline strategy using controller gains and co-contraction levels, previously validated for 11 m/s 2 autonomous braking events [16]. In this baseline strategy, all reference signals, r(t), were Table 1 Lower extremity muscles in the HBM.…”

Section: Maximum Driver Braking Simulationsmentioning

confidence: 99%

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A method to model anticipatory postural control in driver braking events

et al. 2014

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Human body models (HBMs) for vehicle occupant simulations have recently been extended with active muscles and postural control strategies. Feedback control has been used to model occupant responses to autonomous braking interventions. However, driver postural responses during driver initiated braking differ greatly from autonomous braking. In the present study, an anticipatory postural response was hypothesized, modelled in a whole-body HBM with feedback controlled muscles, and validated using existing volunteer data. The anticipatory response was modelled as a time dependent change in the reference value for the feedback controllers, which generates correcting moments to counteract the braking deceleration. The results showed that, in 11 m/s(2) driver braking simulations, including the anticipatory postural response reduced the peak forward displacement of the head by 100mm, of the shoulder by 30 mm, while the peak head flexion rotation was reduced by 18°. The HBM kinematic response was within a one standard deviation corridor of corresponding test data from volunteers performing maximum braking. It was concluded that the hypothesized anticipatory responses can be modelled by changing the reference positions of the individual joint feedback controllers that regulate muscle activation levels. The addition of anticipatory postural control muscle activations appears to explain the difference in occupant kinematics between driver and autonomous braking. This method of modelling postural reactions can be applied to the simulation of other driver voluntary actions, such as emergency avoidance by steering.

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Section: Maximum Driver Braking Simulationsmentioning

confidence: 99%

Section: Musculoskeletal Feedback Control Modelmentioning

confidence: 99%

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A method to model anticipatory postural control in driver braking events

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“…Based on simulating a validated human body model, dynamic conditions and critical situations can be efficiently analysed without posing a risk to the volunteers. Estimating skeletal muscle forces that influence human body motion can provide an insight into the average or person-specific active response, injury probability, and interaction of the human body with safety restraints, as well as assessment of driving ergonomics and comfort [11,12,13]. …”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Measuring Muscle Mechanics in Vehicle Collision Conditions

Kraśna

Đorđević

Hribernik

et al. 2017

Sensors

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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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“…Depending on the scientific question, it is possible to create whole body models for humans [11,12] or parts of the body like the human heart [13,14] or the spine [15,16]. A distinction can be made between the multibody simulation (MBS) modeling and the finite element (FE) modeling.…”

Section: Biomechanical Modeling and Simulationmentioning

confidence: 99%

Computational Simulation as an Innovative Approach in Personalized Medicine

Bauer¹,

Dietrich²

2017

Innovations in Spinal Deformities and Postural Disorders

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Background: Statistical analyses show that both the spine curvature and the morphological properties of the vertebral bodies can differ considerably. Therefore, the best outcome of a surgery for the individual patient could be achieved by developing patient specific implants to prevent inadequate anchorage of implants that don't optimally fit to the anatomy and can cause damages of spinal structures.Objective: The aim of our work is to develop patient-specific biomechanical simulation models of the spine to determine the patient-specific load situation and to open up the possibility of developing implant designs that are adapted to the morphological contours of the patients' endplates, which ensures an optimal fit and a balanced load distribution.Methods: Our approach is the scientific fusion of the fields "medical imaging" and "biomechanical computer modeling." The individual characteristics of patients will be visualized and analyzed through medical imaging. The surface models together with the estimated biomechanical properties can be transferred into biomechanical multibody simulation models.Results: Taking the patient-specific characteristics and the material properties of the implant into account, simulation models are created to simulate preoperatively the biomechanical effects of new implant designs. In this way, the load situation of the modeled spinal structures can be determined.

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A Human Body Model With Active Muscles for Simulation of Pretensioned Restraints in Autonomous Braking Interventions

Cited by 46 publications

References 20 publications

A method to model anticipatory postural control in driver braking events

A method to model anticipatory postural control in driver braking events

A Novel Approach to Measuring Muscle Mechanics in Vehicle Collision Conditions

Computational Simulation as an Innovative Approach in Personalized Medicine

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