Probabilistic sensor network model to assess machine learning accuracy outcomes, by Joseph Russell and Jeroen Bergmann, narrated in full by Joseph Russell - EMBC 2020

Probabilistic sensor network model to assess machine learning accuracy outcomes, by Joseph Russell and Jeroen Bergmann, narrated in full by Joseph Russell, as presented at EMBC on 18th July 2020. Full article available at: https://portal.findresearcher.sdu.dk/da/publications/probabilistic-sensor-network-model-to-assess-machine-learning-acc/ This video is part of an initiative by SDU's Biomedical Engineering Centre to improve the accessibility of scientific papers with human-narrated article readings. The text is read out in full in the same manner as a non-fiction unabridged audiobook, with the article displayed page-by-page on-screen for those wishing to see figures, equations and references. Performance of sensor networks can be predicted using probabilistic modelling (PM). This gives an upper bound on the accuracy for a given set of sensors. Incorrect application of machine learning (ML) can be detected when the ML approach yields accuracy levels greater than the upper bound of the PM. This paper demonstrates experimentally how PM can be used for detecting false ML performance outcomes. This probabilistic method can help identify and correct ML issues in datasets obtained from sensor networks such as wearable medical devices. © 2020 by the authors. #biomedicalengineering #academia #scientificwriting #audiobook #probabilisticsensornetwork #sensorfusion #machinelearning