LEADER 04210nam 22006615 450 001 9910906293803321 005 20251116213517.0 010 $a9783031718212 010 $a3031718216 024 7 $a10.1007/978-3-031-71821-2 035 $a(MiAaPQ)EBC31758453 035 $a(Au-PeEL)EBL31758453 035 $a(CKB)36516541900041 035 $a(DE-He213)978-3-031-71821-2 035 $a(OCoLC)1467956770 035 $a(EXLCZ)9936516541900041 100 $a20241108d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRecent Advances in Machine Learning Techniques and Sensor Applications for Human Emotion, Activity Recognition and Support /$fedited by Kyandoghere Kyamakya, Fadi Al Machot, Habib Ullah, Florenc Demrozi 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (290 pages) 225 1 $aStudies in Computational Intelligence,$x1860-9503 ;$v1175 311 08$a9783031718205 311 08$a3031718208 327 $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. 330 $aThis book explores integrating machine learning techniques and sensor applications for human emotion and activity recognition, creating personalized and effective support systems. It covers state-of-the-art machine learning techniques and large language models using multimodal sensors. Enhancing the quality of life for individuals with special needs, particularly the elderly, is a key focus in Active and Assisted Living (AAL) research. Unlike other literature, it emphasizes support mechanisms along with recognition, using metamodel integration for adaptable AAL systems. This book offers insights into technologies transforming AAL for researchers, students, and practitioners. It is a valuable resource for developing responsive and personalized support systems that enhance life quality in smart environments. It is also essential for advancing the understanding of machine learning and sensor technologies in AAL and emotion recognition. . 410 0$aStudies in Computational Intelligence,$x1860-9503 ;$v1175 606 $aComputational intelligence 606 $aMachine learning 606 $aUser interfaces (Computer systems) 606 $aHuman-computer interaction 606 $aComputational Intelligence 606 $aMachine Learning 606 $aUser Interfaces and Human Computer Interaction 615 0$aComputational intelligence. 615 0$aMachine learning. 615 0$aUser interfaces (Computer systems) 615 0$aHuman-computer interaction. 615 14$aComputational Intelligence. 615 24$aMachine Learning. 615 24$aUser Interfaces and Human Computer Interaction. 676 $a006.3 700 $aKyamakya$b Kyandoghere$01294150 701 $aAl Machot$b Fadi$01775573 701 $aUllah$b Habib$01775574 701 $aDemrozi$b Florenc$01775575 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910906293803321 996 $aRecent Advances in Machine Learning Techniques and Sensor Applications for Human Emotion, Activity Recognition and Support$94290215 997 $aUNINA