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Pressure-Specific Feature Selection for Acute Stress Detection From Physiological Recordings.

, , , , , and . SMC, page 2341-2346. IEEE, (2018)

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Selection of the Most Relevant Physiological Features for Classifying Emotion., , , , , and . PhyCS, page 17-25. SciTePress, (2015)A comparison of wearable and stationary sensors for stress detection., , , and . SMC, page 4362-4366. IEEE, (2016)Multi-modal Fusion for Continuous Emotion Recognition by Using Auto-Encoders., , , and . MuSe @ ACM Multimedia, page 21-27. ACM, (2021)A Multi-feature Fuzzy Index to Assess Stress Level from Bio-signals., , , , , and . EMBC, page 1086-1089. IEEE, (2018)Driver Visual Distraction Detection Using Unsupervised Learning Techniques., , , , and . ITSC, page 945-950. IEEE, (2023)Human Initiated Grasp Space Exploration Algorithm for an Underactuated Robot Gripper Using Variational Autoencoder., , , and . ICRA, page 2598-2604. IEEE, (2021)Tactile texture recognition with a 3-axial force MEMS integrated artificial finger., , , , , and . Robotics: Science and Systems, The MIT Press, (2009)A Multi-User Multi-Task Model For Stress Monitoring From Wearable Sensors., , , , , and . FUSION, page 761-766. IEEE, (2018)Pattern recognition with spiking neurons: performance enhancement based on a statistical analysis., , , and . IJCNN, page 1876-1880. IEEE, (1999)A study of the Dream Net model robustness across continual learning scenarios., , , and . ICDM (Workshops), page 824-833. IEEE, (2022)