The mechanical properties of the skin are important for various applications. Numerous tests have been conducted to characterize the mechanical behavior of this tissue, and this article presents a review on different experimental methods used. A discussion on the general mechanical behavior of the skin, including nonlinearity, viscoelasticity, anisotropy, loading history dependency, failure properties, and aging effects, is presented. Finally, commonly used constitutive models for simulating the mechanical response of skin are discussed in the context of representing the empirically observed behavior.

Linking head kinematics to injury risk has been the focus of numerous brain injury criteria. Although many early forms were developed using mechanics principles, recent criteria have been developed using empirical methods based on subsets of head impact data. In this study, a single-degree-of-freedom (sDOF) mechanical analog was developed to parametrically investigate the link between rotational head kinematics and brain deformation. Model efficacy was assessed by comparing the maximum magnitude of displacement to strain-based brain injury predictors from finite element (FE) human head models. A series of idealized rotational pulses covering a broad range of acceleration and velocity magnitudes (0.1-15 krad/s2 and 1-100 rad/s) with durations between 1 and 3000 ms were applied to the mechanical models about each axis of the head. Results show that brain deformation magnitude is governed by three categories of rotational head motion each distinguished by the duration of the pulse relative to the brain's natural period: for short-duration pulses, maximum brain deformation depended primarily on angular velocity magnitude; for long-duration pulses, brain deformation depended primarily on angular acceleration magnitude; and for pulses relatively close to the natural period, brain deformation depended on both velocity and acceleration magnitudes. These results suggest that brain deformation mechanics can be adequately explained by simple mechanical systems, since FE model responses and experimental brain injury tolerances exhibited similar patterns to the sDOF model. Finally, the sDOF model was the best correlate to strain-based responses and highlighted fundamental limitations with existing rotational-based brain injury metrics.

Gregory J. Tierney [1] , Hamed Joodaki [2] , Tron Krosshaug [3] , Jason L. Forman [2] , Jeff R. Crandall [2] , Ciaran K. Simms [1] [1] Trinity Centre for Bioengineering, Trinity College Dublin, Ireland [2] Centre for Applied Biomechanics, University of Virginia, United States of America impacts, but velocity data is less reliable. MBIM data, combined in future with velocity/acceleration data from wearable sensors could be used to provide input conditions and evaluate the outputs of multibody and finite element head models for brain injury assessment of sporting head impacts.

No abstract

Approximately 1.6–3.8 million sports-related traumatic brain injuries occur each year in the U.S. Researchers track the head motion using a variety of techniques to study the head injury biomechanics. To understand how helmets provide head protection, quantification of the relative motion between the head and the helmet is necessary. The purpose of this study was to compare helmet and head kinematics and quantify the relative motion of helmet with respect to head during experimental representations of on-field American football impact scenarios. Seven helmet-to-helmet impact configurations were simulated by propelling helmeted crash test dummies into each other. Head and helmet kinematics were measured with instrumentation and an optical motion capture system. The analysis of results, from 10 ms prior to the helmet contact to 20 ms after the loss of helmet contact, showed that the helmets translated 12–41 mm and rotated up to 37 deg with respect to the head. The peak resultant linear acceleration of the helmet was about 2–5 times higher than the head. The peak resultant angular velocity of the helmet ranged from 37% less to 71% more than the head, depending on the impact conditions. The results of this study demonstrate that the kinematics of the head and the helmet are noticeably different and that the helmet rotates significantly with respect to the head during impacts. Therefore, capturing the helmet kinematics using a video motion tracking methodology is not sufficient to study the biomechanics of the head. Head motion must be measured independently of the helmet.