首页|Recent Studies from University of Lubeck Add New Data to Machine Learning (An Ex perimental and Clinical Physiological Signal Dataset for Automated Pain Recognit ion)
Recent Studies from University of Lubeck Add New Data to Machine Learning (An Ex perimental and Clinical Physiological Signal Dataset for Automated Pain Recognit ion)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in artific ial intelligence. According to news originating from the University of Lubeck by NewsRx correspondents, research stated, “Access to large amounts of data is ess ential for successful machine learning research.” Financial supporters for this research include German Federal Ministry of Educat ion And Research; Polish Ministry of Science, Poland. The news editors obtained a quote from the research from University of Lubeck: “ However, there is insufficient data for many applications, as data collection is often challenging and time-consuming. The same applies to automated pain recogn ition, where algorithms aim to learn associations between a level of pain and be havioural or physiological responses. Although machine learning models have show n promise in improving the current gold standard of pain monitoring (self-report s) only a handful of datasets are freely accessible to researchers. This paper p resents the PainMonit Dataset for automated pain detection using physiological d ata. The dataset consists of two parts, as pain can be perceived differently dep ending on its underlying cause. (1) Pain was triggered by heat stimuli in an exp erimental study during which nine physiological sensor modalities (BVP, 2 x EDA, skin temperature, ECG, EMG, IBI, HR, respiration) were recorded from 55 healthy subjects.”
University of LubeckCyborgsEmerging TechnologiesMachine Learning
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