We focus on the problem of estimating human hand-tremor frequency from input RGB video data. Estimating tremors from video is important for non-invasive monitoring, analyzing and diagnosing patients suffering from motor-disorders such as Parkinson’s disease. We consider two approaches for hand-tremor frequency estimation: (a) a Lagrangian approach where we detect the hand at every frame in the video, and estimate the tremor frequency along the trajectory; and (b) an Eulerian approach where we first localize the hand, we subsequently remove the large motion along the movement trajectory of the hand, and we use the video information over time encoded as intensity values or phase information to estimate the tremor frequency. We estimate hand tremors on a new human tremor dataset, TIM-Tremor, containing 55 tremor patient recordings together with: associated ground truth accelerometer data from the most affected hand, RGB video data, and aligned depth data, as well as a multitude of more dynamic tasks, involving larger motion of the hands.

High-performance concrete (HPC) is vulnerable to spalling under high temperature conditions and it has been found that the heating rate can exert a tremendous effect on spalling of HPC. To prevent HPC from spalling, the heating rate effect should be understood. However, quantitative analyses are still lacking and the heating rate effect has not been well interpreted so far. In this paper, a numerical analysis of the heating rate effect on spalling of HPC is presented. Based on the experimental results reported in the literature, the spalling behavior of cubic HPC specimens under fire heating and slow heating with a heating rate of 5 °C/min is modeled. With a meso-level thermo-chemo-hydro-mechanical analysis, the temperature gradient induced thermal stress and the mechanical effect of build-up vapor pressure are investigated. The results show that, at different heating rates, the spalling mechanisms are different. Finally, possible manners and mechanisms of spalling are discussed.

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