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Bioreactors with a controlled physiological environment are being developed to study various cell processes. The influences of mechanostimulation on bone cell cultures can be investigated using a compression bioreactor. The developed bioreactor system applies a cyclic compression force to the specimen via an eccentrically mounted push rod. The compression force is monitored by a force sensor to detect changes in the material properties of the specimen. Depending on the piston setting, a stroke of 0.28 - 2.50 mm can be applied to the specimen. The bioreactor system was tested with a trial run of 18 days. A sample was continuously stimulated with a loading frequency of 2 Hz and a stroke of 1.50 mm. The sterility in the cell chamber as well as the functionality of the realised bioreactor stimulation system could be successfully confirmed

Automatic surgical skill assessment in robotic surgery based on video data is essential in facilitating faster learning curves for trainees while relieving expert surgeons from the time- and cost-intensive feedback process. Recent years have shown several advancements in this area by utilizing deep learning. While current research focuses on novel architectures, the influence of video-preprocessing on their performance remains unknown. In this work, we present the first investigation on the influence of video-preprocessing on deep learning-based surgical skill assessment. Thus, we integrated four preprocessing modules, i.e. Deblurring, Segmentbased Sampling, Optical Flow and the Combination of all of them, into skill assessment on the JIGSAWS dataset using a well-established network architecture. Despite all single preprocessing steps showing no clear improvement, the Combination of all steps showed higher median performance and lower variance. Furthermore, we performed frame-wise investigations on the influence of optical flow artifacts and their reduction in the combined setting. Our results highlight the potential of well-calibrated video-preprocessing for automatic surgical skill assessment.