04196nam a22005055i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050000900153072001600162072002300178072001500201245023100216250001800447264006900465300006400534336002600598337002600624338003600650347002400686490006200710505094000772520095301712650003202665650002202697650004002719650003202759650003202791650002202823650005202845700015102897700014503048700014203193700007903335710003403414773002603448776003603474776003603510776003603546830006203582856004603644978-3-031-71821-2DE-He21320251030114911.0cr nn 008mamaa241108s2024    sz |    s    |||| 0|eng d  a97830317182127 a10.1007/978-3-031-71821-22doi 4aQ342 7aUYQ2bicssc 7aCOM0040002bisacsh 7aUYQ2thema10aRecent Advances in Machine Learning Techniques and Sensor Applications for Human Emotion, Activity Recognition and Supporth[electronic resource] /cedited by Kyandoghere Kyamakya, Fadi Al Machot, Habib Ullah, Florenc Demrozi.  a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXV, 278 p. 92 illus., 87 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aStudies in Computational Intelligence,x1860-9503 ;v11750 aDecoding Human Essence Novel Machine Learning Techniques and Sensor Applications in Emotion Perception and Activity Detection -- Leveraging Context-Aware Emotion and Fatigue Recognition through Large Language Models for Enhanced Advanced Driver Assistance Systems ADAS -- ECG based Human Emotion Recognition Using Generative Models -- An evolutionary convolutional neural network architecture for recognizing emotions from EEG signals -- Analyzing the Potential Contribution of a Meta Learning Approach to Robust and Effective Subject Independent Emotion related Time Series Analysis of Bio signals -- A Multibranch LSTM CNN Model for Human Activity Recognition -- Importance of Activity and Emotion Detection in the field of Ambient Assisted Living -- Real Time Human Activity Recognition for the Elderly VR Training with Body Area Networks -- An Interactive Metamodel Integration Approach IMIA for Active and Assisted Living Systems.  aThis book explores integrating machine learning techniques and sensor applications for human emotion and activity recognition, creating personalized and effective support systems. It covers state-of-the-art machine learning techniques and large language models using multimodal sensors. Enhancing the quality of life for individuals with special needs, particularly the elderly, is a key focus in Active and Assisted Living (AAL) research. Unlike other literature, it emphasizes support mechanisms along with recognition, using metamodel integration for adaptable AAL systems. This book offers insights into technologies transforming AAL for researchers, students, and practitioners. It is a valuable resource for developing responsive and personalized support systems that enhance life quality in smart environments. It is also essential for advancing the understanding of machine learning and sensor technologies in AAL and emotion recognition. . 0aComputational intelligence. 0aMachine learning. 0aUser interfaces (Computer systems). 0aHuman-computer interaction.14aComputational Intelligence.24aMachine Learning.24aUser Interfaces and Human Computer Interaction.1 aKyamakya, Kyandoghere.eeditor.0(orcid)0000-0003-0773-94761https://orcid.org/0000-0003-0773-94764edt4http://id.loc.gov/vocabulary/relators/edt1 aAl Machot, Fadi.eeditor.0(orcid)0000-0002-1239-92611https://orcid.org/0000-0002-1239-92614edt4http://id.loc.gov/vocabulary/relators/edt1 aUllah, Habib.eeditor.0(orcid)0000-0002-2434-08491https://orcid.org/0000-0002-2434-08494edt4http://id.loc.gov/vocabulary/relators/edt1 aDemrozi, Florenc.eeditor.4edt4http://id.loc.gov/vocabulary/relators/edt2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303171820508iPrinted edition:z978303171822908iPrinted edition:z9783031718236 0aStudies in Computational Intelligence,x1860-9503 ;v117540uhttps://doi.org/10.1007/978-3-031-71821-2