The photoelastic response of periodic arrays of stripes attached to the surface of a substrate and illuminated by an ultrashort laser pulse were investigated. The samples were gold arrays on silicon and aluminum arrays either on crystalline quartz or on silicon. The metallic stripes had submicrometer lateral dimensions and the spatial periods ranged from about 1 microm up to 5 microm. The substrate being transparent (quartz) or slightly absorbing (silicon) at the laser wavelength (lambda = 750 nm), a laterally modulated thermal stress is generated near the surface of the substrate when a light pulse illuminates the structure. The studies of vibrations involved by the subsequent relaxation processes show that surface acoustic waves at frequency as high as about 5 GHz are excited with the samples consisting of aluminum stripes. In the case of the aluminum samples with the largest lateral spatial periods (aluminum on quartz), the surface acoustic wave propagates outside the illuminated area. In the case of the gold samples, a normal mode of individual bars is observed instead. Experimental evidence shows that these behaviors are mainly governed both by the lateral spatial period of the structure and by the density of the metal.

Estimating joint kinematics from skin-marker trajectories recorded using stereophotogrammetry is complicated by soft tissue artefact (STA), an inexorable source of error. One solution is to use a bone pose estimator based on multi-body kinematics optimisation (MKO) embedding joint constraints to compensate for STA. However, there is some debate over the effectiveness of this method. The present study aimed to quantitatively assess the degree of agreement between reference (i.e., artefact-free) knee joint kinematics and the same kinematics estimated using MKO embedding six different knee joint models. The following motor tasks were assessed: level walking, hopping, cutting, running, sit-to-stand, and step-up. Reference knee kinematics was taken from pin-marker or biplane fluoroscopic data acquired concurrently with skin-marker data, made available by the respective authors. For each motor task, Bland-Altman analysis revealed that the performance of MKO varied according to the joint model used, with a wide discrepancy in results across degrees of freedom (DoFs), models and motor tasks (with a bias between -10.2° and 13.2° and between -10.2mm and 7.2mm, and with a confidence interval up to ±14.8° and ±11.1mm, for rotation and displacement, respectively). It can be concluded that, while MKO might occasionally improve kinematics estimation, as implemented to date it does not represent a reliable solution to the STA issue.

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