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The article presents a new technique for measuring paper deformation in unidirectional tensile tests, based on recording and analysis of a series of specimen images. The proposed technique differs from the DIC-based deformation measurement in that the cross-correlation of image data has been replaced with linear filtering. For this purpose, a regular grid of markers is printed on the sample. Filtering the image creates local maxima in the places where markers occur. The developed algorithm finds their location with sub-pixel accuracy. Printing a grid of markers on tested paper and use of reference objects visible in the same image as the paper sample, freed from the need to mechanically connect the camera and the universal testing machine and from the necessity to electronically synchronize their work. The obtained deformation distributions and Poisson's ratios are in accordance with the literature data which confirms the correctness of the developed measurement technique. [ABSTRACT FROM AUTHOR]

Access to large amounts of data is essential for successful machine learning research. However, there is insufficient data for many applications, as data collection is often challenging and time-consuming. The same applies to automated pain recognition, where algorithms aim to learn associations between a level of pain and behavioural or physiological responses. Although machine learning models have shown promise in improving the current gold standard of pain monitoring (self-reports) only a handful of datasets are freely accessible to researchers. This paper presents the PainMonit Dataset for automated pain detection using physiological data. The dataset consists of two parts, as pain can be perceived differently depending on its underlying cause. (1) Pain was triggered by heat stimuli in an experimental study during which nine physiological sensor modalities (BVP, 2×EDA, skin temperature, ECG, EMG, IBI, HR, respiration) were recorded from 55 healthy subjects. (2) Eight modalities (2×BVP, 2×EDA, EMG, skin temperature, respiration, grip) were recorded from 49 participants to assess their pain during a physiotherapy session., (© 2024. The Author(s).)