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245 1 0 _aRecent Advances in Machine Learning Techniques and Sensor Applications for Human Emotion, Activity Recognition and Support
_h[electronic resource] /
_cedited by Kyandoghere Kyamakya, Fadi Al Machot, Habib Ullah, Florenc Demrozi.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXV, 278 p. 92 illus., 87 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
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338 _aonline resource
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490 1 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v1175
505 0 _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.
520 _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. .
541 _fUABC ;
_cPerpetuidad
650 0 _aComputational intelligence.
650 0 _aMachine learning.
650 0 _aUser interfaces (Computer systems).
650 0 _aHuman-computer interaction.
650 1 4 _aComputational Intelligence.
650 2 4 _aMachine Learning.
650 2 4 _aUser Interfaces and Human Computer Interaction.
700 1 _aKyamakya, Kyandoghere.
_eeditor.
_0(orcid)0000-0003-0773-9476
_1https://orcid.org/0000-0003-0773-9476
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aAl Machot, Fadi.
_eeditor.
_0(orcid)0000-0002-1239-9261
_1https://orcid.org/0000-0002-1239-9261
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aUllah, Habib.
_eeditor.
_0(orcid)0000-0002-2434-0849
_1https://orcid.org/0000-0002-2434-0849
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aDemrozi, Florenc.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031718205
776 0 8 _iPrinted edition:
_z9783031718229
776 0 8 _iPrinted edition:
_z9783031718236
830 0 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v1175
856 4 0 _zLibro electrónico
_uhttp://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-71821-2
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