04618nam a22005895i 4500 978-3-031-71821-2 DE-He213 20251030114911.0 cr nn 008mamaa 241108s2024 sz | s |||| 0|eng d 9783031718212 978-3-031-71821-2 10.1007/978-3-031-71821-2 doi Q342 UYQ bicssc COM004000 bisacsh UYQ thema Recent Advances in Machine Learning Techniques and Sensor Applications for Human Emotion, Activity Recognition and Support [electronic resource] / edited by Kyandoghere Kyamakya, Fadi Al Machot, Habib Ullah, Florenc Demrozi. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XV, 278 p. 92 illus., 87 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Studies in Computational Intelligence, 1860-9503 ; 1175 Decoding 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. This 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. . Computational intelligence. 64052 Machine learning. 55059 User interfaces (Computer systems). 64688 Human-computer interaction. 64854 Computational Intelligence. 64052 Machine Learning. 55059 User Interfaces and Human Computer Interaction. 66577 Kyamakya, Kyandoghere. editor. (orcid)0000-0003-0773-9476 https://orcid.org/0000-0003-0773-9476 edt http://id.loc.gov/vocabulary/relators/edt 66070 Al Machot, Fadi. editor. (orcid)0000-0002-1239-9261 https://orcid.org/0000-0002-1239-9261 edt http://id.loc.gov/vocabulary/relators/edt 89944 Ullah, Habib. editor. (orcid)0000-0002-2434-0849 https://orcid.org/0000-0002-2434-0849 edt http://id.loc.gov/vocabulary/relators/edt 89945 Demrozi, Florenc. editor. edt http://id.loc.gov/vocabulary/relators/edt 89946 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031718205 Printed edition: 9783031718229 Printed edition: 9783031718236 Studies in Computational Intelligence, 1860-9503 ; 1175 68854 https://doi.org/10.1007/978-3-031-71821-2 ZDB-2-INR ZDB-2-SXIT 66224 66224