A Novel Neuromuscular Head-Neck Model and Its Application on Impact Analysis

Modeling and Analysis of Environmental Electromagnetic Interference in Multiple-Channel Neural Recording Systems for High Common-Mode Interference Rejection Performance

Environmental electromagnetic interference (EMI) has always been a major interference source for multiple-channel neural recording systems, and little theoretical work has been attempted to address it. In this paper, equivalent circuit models are proposed to model both electromagnetic interference sources and neural signals in such systems, and analysis has been performed to generate the design guidelines for neural probes and the subsequent recording circuit towards higher common-mode interference (CMI) rejection performance while maintaining the recorded neural action potential (AP) signal quality. In vivo animal experiments with a configurable 32-channel neural recording system are carried out to validate the proposed models and design guidelines. The results show the power spectral density (PSD) of environmental 50 Hz EMI interference is reduced by three orders from 4.43 × 10-3 V2/Hz to 4.04 × 10-6 V2/Hz without affecting the recorded AP signal quality in an unshielded experiment environment.

Investigation on vehicle occupant dummy applicability for under-foot impact loading conditions

PURPOSE

Under-foot impact loadings can cause serious lower limb injuries in many activities, such as automobile collisions and underbody explosions to military vehicles. The present study aims to compare the biomechanical responses of the mainstream vehicle occupant dummies with the human body lower limb model and analyze their robustness and applicability for assessing lower limb injury risk in under-foot impact loading environments.

METHODS

The Hybrid III model, the test device for human occupant restraint (THOR) model, and a hybrid human body model with the human active lower limb model were adopted for under-foot impact analysis regarding different impact velocities and initial lower limb postures.

RESULTS

The results show that the 2 dummy models have larger peak tibial axial force and higher sensitivity to the impact velocities and initial postures than the human lower limb model. In particular, the Hybrid III dummy model presented extremely larger peak tibial axial forces than the human lower limb model. In the case of minimal difference in tibial axial force, Hybrid III's tibial axial force (7.5 KN) is still 312.5% that of human active lower limb's (2.4 KN). Even with closer peak tibial axial force values, the biomechanical response curve shapes of the THOR model show significant differences from the human lower limb model.

CONCLUSION

Based on the present results, the Hybrid III dummy cannot be used to evaluate the lower limb injury risk in under-foot loading environments. In contrast, potential improvement in ankle biofidelity and related soft tissues of the THOR dummy can be implemented in the future for better applicability.

A neuromuscular human body model for lumbar injury risk analysis in a vibration loading environment

BACKGROUND AND OBJECTIVE

Long-term intensive exposure to whole-body vibration substantially increases the risk of low back pain and degenerative diseases in special occupational groups, like motor vehicle drivers, military vehicle occupants, aircraft pilots, etc. This study aims to establish and validate a neuromuscular human body model focusing on improvement of the detailed description of anatomic structures and neural reflex control, for lumbar injury analysis in vibration loading environments.

METHODS

A whole-body musculoskeletal in Opensim codes was first improved by including a detailed anatomic description of spinal ligaments, non-linear intervertebral disc, and lumbar facet joints, and coupling a proprioceptive feedback closed-loop control strategy with GTOs and muscle spindles modeling in Python codes. Then, the established neuromuscular model was multi-levelly validated from sub-segments to the whole model, from regular movements to dynamic responses to vibration loadings. Finally, the neuromuscular model was combined with a dynamic model of an armored vehicle to analyze occupant lumbar injury risk in vibration loadings due to different road conditions and traveling velocities.

RESULT

Based on a series of biomechanical indexes, including lumbar joint rotation angles, the lumbar intervertebral pressures, the displacement of the lumbar segments, and the lumbar muscle activities, the validation results show that the present neuromuscular model is available and feasible in predicting lumbar biomechanical responses in normal daily movement and vibration loading environments. Furthermore, the combined analysis with the armored vehicle model predicted similar lumbar injury risk to the experimental or epidemiologic studies. The preliminary analysis results also showed that road types and travelling velocities have substantial combined effects on lumbar muscle activities, and indicated that intervertebral joint pressure and muscle activity indexes can need to be jointly considered for lumbar injury risk evaluation.

CONCLUSION

In conclusion, the established neuromuscular model is an effective tool to evaluate vibration loading effects on injury risk of the human body and assist vehicle design vibration comfort by directly concerning the human body injury itself.

Should anthropometric differences between the commonly used pedestrian computational biomechanics models and Chinese population be taken into account when predicting pedestrian head kinematics and injury in vehicle collisions in China?

Computational biomechanics models play a key role in predicting/evaluating pedestrian head kinematics and injury risk in car-to-pedestrian collisions. The human multibody models most commonly used in car-to-pedestrian collision reconstruction, such as pedestrian model by The Netherlands Organisation for Applied Scientific Research TNO, are built using the anthropometry of Western European population as defined in TNO (2013) human multibody model database. In this study, we investigate the effects of the anthropometric differences between the Western European and Chinese populations on the pedestrian head kinematics and injury responses predicted using multibody models. The comparison was conducted through car-to-pedestrian collision simulations using pedestrian multibody models representing anthropometric characteristics of Western European and Chinese populations, three typical vehicle shapes (sedan, SUV and minivan), five initial vehicle impact speeds (30, 35, 40, 45, 50 km/h), and six pedestrian walking postures. The results indicate that the change of pedestrian model anthropometry (from Western European to Chinese) exerts appreciable effects on both the predicted initial boundary conditions of the head-to-windscreen impact (in particular the head-to-windscreen impact angle) and the head injury indices in the impact with the road surface (secondary impact).

‘Falling heads’: investigating reflexive responses to head–neck perturbations

Background

Reflexive responses to head–neck perturbations affect the injury risk in many different situations ranging from sports-related impact to car accident scenarios. Although several experiments have been conducted to investigate these head–neck responses to various perturbations, it is still unclear why and how individuals react differently and what the implications of these different responses across subjects on the potential injuries might be. Therefore, we see a need for both experimental data and biophysically valid computational Human Body Models with bio-inspired muscle control strategies to understand individual reflex responses better.

Methods

To address this issue, we conducted perturbation experiments of the head–neck complex and used this data to examine control strategies in a simulation model. In the experiments, which we call ’falling heads’ experiments, volunteers were placed in a supine and a prone position on a table with an additional trapdoor supporting the head. This trapdoor was suddenly released, leading to a free-fall movement of the head until reflexive responses of muscles stopped the downwards movement.

Results

We analysed the kinematic, neuronal and dynamic responses for all individuals and show their differences for separate age and sex groups. We show that these results can be used to validate two simple reflex controllers which are able to predict human biophysical movement and modulate the response necessary to represent a large variability of participants.

Conclusions

We present characteristic parameters such as joint stiffness, peak accelerations and latency times. Based on this data, we show that there is a large difference in the individual reflexive responses between participants. Furthermore, we show that the perturbation direction (supine vs. prone) significantly influences the measured kinematic quantities. Finally, ’falling heads’ experiments data are provided open-source to be used as a benchmark test to compare different muscle control strategies and to validate existing active Human Body Models directly.