Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Jaakko Tervonen"'
Publikováno v:
Frontiers in Neuroergonomics, Vol 4 (2023)
IntroductionCurrent stress detection methods concentrate on identification of stress and non-stress states despite the existence of various stress types. The present study performs a more specific, explainable stress classification, which could provi
Externí odkaz:
https://doaj.org/article/d6944855241a48db89fee97b6482f125
Publikováno v:
Nath, R K, Tervonen, J, Närväinen, J, Pettersson, K & Mäntyjärvi, J 2023, Towards Self-Supervised Learning of ECG Signal Representation for the Classification of Acute Stress Types . in GLSVLSI 2023-Proceedings of the Great Lakes Symposium on VLSI 2023 . Association for Computing Machinery ACM, pp. 85-90 . https://doi.org/10.1145/3583781.3590252
We present a novel application of contrastive learning technique in learning the feature representation of ECG signal in a self-supervised manner for the classification of acute stress types. Acute stress types that occur for a very short period and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::16152e427cc5a53f078d87972cf2359f
https://cris.vtt.fi/en/publications/6a473b0e-108d-4734-8685-0e6bd61767f7
https://cris.vtt.fi/en/publications/6a473b0e-108d-4734-8685-0e6bd61767f7
Autor:
Arttu Lamsa, Jaakko Tervonen, Jussi Liikka, Constantino Alvarez Casado, Miguel Bordallo Lopez
Publikováno v:
Lämsä, A, Tervonen, J, Liikka, J, Casado, C A & Bordallo Lopez, M 2022, Video2IMU : Realistic IMU features and signals from videos . in 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics and IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks : Proceedings . IEEE Institute of Electrical and Electronic Engineers, International Workshop on Wearable and Implantable Body Sensor Networks (BSN), IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks, BSN 2022, Ioannina, Greece, 27/09/22 . https://doi.org/10.1109/BSN56160.2022.9928466
Human Activity Recognition (HAR) from wearable sensor data identifies movements or activities in unconstrained environments. HAR is a challenging problem as it presents great variability across subjects. Obtaining large amounts of labelled data is no
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::af78da0bbb80c39e73f3c5e1121c61be
https://cris.vtt.fi/en/publications/adb0083e-f76b-4332-b21b-a423d849435d
https://cris.vtt.fi/en/publications/adb0083e-f76b-4332-b21b-a423d849435d
Publikováno v:
UbiComp/ISWC Adjunct
Tervonen, J, Närväinen, J, Mäntyjärvi, J & Pettersson, K 2021, In Search of Harmful Stress . in UbiComp/ISWC 2021-Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers . Association for Computing Machinery ACM, pp. 215-217, 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2021 ACM International Symposium on Wearable Computers, UbiComp/ISWC 2021, Virtual, Online, United States, 21/09/21 . https://doi.org/10.1145/3460418.3479335
Tervonen, J, Närväinen, J, Mäntyjärvi, J & Pettersson, K 2021, In Search of Harmful Stress . in UbiComp/ISWC 2021-Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers . Association for Computing Machinery ACM, pp. 215-217, 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2021 ACM International Symposium on Wearable Computers, UbiComp/ISWC 2021, Virtual, Online, United States, 21/09/21 . https://doi.org/10.1145/3460418.3479335
Human body produces different physiological stress reaction when you hit a toe to a doorstep than when you panic at a job interview. The impact for body's homeostasis varies depending on the reaction type and some reactions are harmful to our health.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f755af6e8d002a8e655aeac4e2b31bf0
https://cris.vtt.fi/en/publications/d7aca490-aaa2-40b0-962e-e86cf61bc941
https://cris.vtt.fi/en/publications/d7aca490-aaa2-40b0-962e-e86cf61bc941
Autor:
Kati Pettersson, Jaakko Tervonen, Johanna Närväinen, Pentti Henttonen, Jani Mäntyjärvi, Ilmari Määttänen
Publikováno v:
BIBE
Pettersson, K, Tervonen, J, Närväinen, J, Henttonen, P, Määttänen, I & Mäntyjärvi, J 2020, Selecting Feature Sets and Comparing Classification Methods for Cognitive State Estimation . in Proceedings-IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020 ., 9288104, IEEE Institute of Electrical and Electronic Engineers, pp. 683-690, 20th IEEE International Conference on BioInformatics And BioEngineering, Cincinnati, Ohio, United States, 26/10/20 . https://doi.org/10.1109/BIBE50027.2020.00115
Pettersson, K, Tervonen, J, Närväinen, J, Henttonen, P, Määttänen, I & Mäntyjärvi, J 2020, Selecting Feature Sets and Comparing Classification Methods for Cognitive State Estimation . in Proceedings-IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020 ., 9288104, IEEE Institute of Electrical and Electronic Engineers, pp. 683-690, 20th IEEE International Conference on BioInformatics And BioEngineering, Cincinnati, Ohio, United States, 26/10/20 . https://doi.org/10.1109/BIBE50027.2020.00115
Acute stress and high workload are part of everyday work at safety critical fields (e.g. health care). Adaptive human computer interaction systems could support and guide a nurse or a doctor in these hectic situations. Seamless interaction between hu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3b2197b52a5ba01072c7ce9c0fcf8d8e
https://doi.org/10.1109/bibe50027.2020.00115
https://doi.org/10.1109/bibe50027.2020.00115
Autor:
Janne Keränen, Jaakko Tervonen, Jani Mäntyjärvi, Zsolt Homorodi, Mikko J. Sillanpää, Arttu Lämsä, Antti Tolonen, Sampsa Puttonen, Janne Pajukanta, Leila Hopsu
Publikováno v:
Tervonen, J, Puttonen, S, Sillanpää, M J, Hopsu, L, Homorodi, Z, Keränen, J, Pajukanta, J, Tolonen, A, Lämsä, A & Mäntyjärvi, J 2020, ' Personalized mental stress detection with self-organizing map : From laboratory to the field ', Computers in Biology and Medicine, vol. 124, 103935 . https://doi.org/10.1016/j.compbiomed.2020.103935
Stress has become a major health concern and there is a need to study and develop new digital means for real-time stress detection. Currently, the majority of stress detection research is using population based approaches that lack the capability to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ea8e39da2b67af6c13f3a5a94fc22240
https://doi.org/10.1016/j.compbiomed.2020.103935
https://doi.org/10.1016/j.compbiomed.2020.103935
Publikováno v:
Electronics, Vol 10, Iss 613, p 613 (2021)
Electronics
Volume 10
Issue 5
Tervonen, J, Pettersson, K & Mäntyjärvi, J 2021, ' Ultra-Short Window Length and Feature Importance Analysis for Cognitive Load Detection from Wearable Sensors ', Electronics, vol. 10, no. 5, 613, pp. 1-19 . https://doi.org/10.3390/electronics10050613
Electronics
Volume 10
Issue 5
Tervonen, J, Pettersson, K & Mäntyjärvi, J 2021, ' Ultra-Short Window Length and Feature Importance Analysis for Cognitive Load Detection from Wearable Sensors ', Electronics, vol. 10, no. 5, 613, pp. 1-19 . https://doi.org/10.3390/electronics10050613
Human cognitive capabilities are under constant pressure in the modern information society. Cognitive load detection would be beneficial in several applications of human–computer interaction, including attention management and user interface adapta
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f2046de3ca838d2a05e86f95f0bbf955
https://doi.org/10.3390/electronics10050613
https://doi.org/10.3390/electronics10050613
Autor:
Jani Koivusaari, Riku Mäkynen, Johannes Peltola, Johanna Kallio, Pauli Räsänen, Jaakko Tervonen
Publikováno v:
Building and Environment
Kallio, J, Tervonen, J, Räsänen, P, Mäkynen, R, Koivusaari, J & Peltola, J 2021, ' Forecasting office indoor CO 2 concentration using machine learning with a one-year dataset ', Building and Environment, vol. 187, 107409 . https://doi.org/10.1016/j.buildenv.2020.107409
Kallio, J, Tervonen, J, Räsänen, P, Mäkynen, R, Koivusaari, J & Peltola, J 2021, ' Forecasting office indoor CO 2 concentration using machine learning with a one-year dataset ', Building and Environment, vol. 187, 107409 . https://doi.org/10.1016/j.buildenv.2020.107409
Modern buildings are expected to fulfill energy efficiency regulations while providing a healthy and comfortable living environment for their occupants. Demand-controlled heating, ventilation, and air conditioning can improve energy efficiency and we
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ce6a93d96c40793fc57486c52dd5e11e