首页|Researcher from Hamad Bin Khalifa University Reports Recent Findings in Machine Learning (Strategies for Reliable Stress Recognition: A Machine Learning Approac h Using Heart Rate Variability Features)
Researcher from Hamad Bin Khalifa University Reports Recent Findings in Machine Learning (Strategies for Reliable Stress Recognition: A Machine Learning Approac h Using Heart Rate Variability Features)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Data detailed on artificial intelligen ce have been presented. According to news originating from Hamad Bin Khalifa Uni versity by NewsRx editors, the research stated, “Stress recognition, particularl y using machine learning (ML) with physiological data such as heart rate variabi lity (HRV), holds promise for mental health interventions.” Funders for this research include Qatar National Research Fund. The news reporters obtained a quote from the research from Hamad Bin Khalifa Uni versity: “However, limited datasets in affective computing and healthcare resear ch can lead to inaccurate conclusions regarding the ML model performance. This s tudy employed supervised learning algorithms to classify stress and relaxation s tates using HRV measures. To account for limitations associated with small datas ets, robust strategies were implemented based on methodological recommendations for ML with a limited dataset, including data segmentation, feature selection, a nd model evaluation. Our findings highlight that the random forest model achieve d the best performance in distinguishing stress from non-stress states. Notably, it showed higher performance in identifying stress from relaxation (F1-score: 8 6.3%) compared to neutral states (F1-score: 65.8%).”
Hamad Bin Khalifa UniversityCyborgsE merging TechnologiesMachine Learning
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