Description
Artificial Intelligence and Multimodal Signal Processing in Human-Machine Interaction presents an overview of an emerging field that is concerned with exploiting multiple modalities of communication in both Artificial Intelligence and Human-Machine Interaction. The book not only provides cross disciplinary research in the fields of multimodal signal acquisition and sensing, analysis, IoTs (Internet of Things), Artificial Intelligence, and system architectures, it also evaluates the role of Artificial Intelligence I in relation to the realization of contemporary Human Machine Interaction (HMI) systems.Readers are introduced to the multimodal signals and their role in the identification of the intended subjects, mental state and the realization of HMI systems are explored, and the applications of signal processing and machine/ensemble/deep learning for HMIs are assessed. A description of proposed methodologies is provided, and related works are also presented. This is a valuable resource for researchers, health professionals, postgraduate students, post doc researchers and faculty members in the fields of HMIs, Brain-Computer Interface (BCI), Prosthesis, Computer vision, and Mental state estimation, and all those who wish to broaden their knowledge in the allied field.- Covers advances in the multimodal signal processing and artificial intelligence assistive HMIs- Presents theories, algorithms, realizations, applications, approaches, and challenges that will have their impact and contribution in the design and development of modern and effective HMI (Human Machine Interaction) system- Presents different aspects of the multimodal signals, from the sensing to analysis using hardware/software, and making use of machine/ensemble/deep learning in the intended problem-solving
Table of Contents
1. Introduction to human-machine interactionSYED SAAD AHMED, HUMAIRA NISAR, AND LO PO KIM2. Artificial intelligence techniques for human-machine interactionHAMID MUKHTAR3. Feature extraction techniques for human-computer interactionABDULHAMIT SUBASI AND SAEED MIAN QAISAR4. An overview of techniques and best practices to create intuitive and user-friendly human-machine interfacesVEERENDRA DAKULAGI, KIM HO YEAP, HUMAIRA NISAR, ROHINI DAKULAGI, G N BASAVARAJ, AND MIGUEL VILLAGOMEZ GALINDO5. An overview of EEG-based human-computer interface (HCI)MD MAHMUDUL HASAN, SITI ARMIZA MOHD ARIS, AND NORIZAM SULAIMAN6. Speech-driven human-machine interaction using Mel-frequency Cepstral coefficients with machine learning and CymaticsSAEED MIAN QAISAR7. EEG-based brain-computer interface using wavelet packet decomposition and ensemble classifiersABDULHAMIT SUBASI AND SAEED MIAN QAISAR8. Understanding dyslexia and the potential of AI in detecting neurocognitive impairment in dyslexiaSITI ATIYAH ALI, HUMAIRA NISAR, NURFAIZATUL AISYAH AB AZIZ, NOR ASYIKIN FADZIL, NUR SAIDA MOHAMAD ZABER, AND LUTHFFI IDZHAR ISMAIL9. Early dementia detection and severity classification with deep SqueezeNet convolutional neural network using EEG imagesNOOR KAMAL AL-QAZZAZ, SAWAL HAMID BIN MOHD ALI, AND SITI ANOM AHMAD10. EEG-based stress identification using oscillatory mode decomposition and artificial neural networkSARIKA KHANDELWAL, NILIMA SALANKAR, AND SAEED MIAN QAISAR11. EEG signal processing with deep learning for alcoholism detectionHAMID MUKHTAR12. Machine learning and signal processing for ECG-based emotion recognitionFADIME TOKMAK, AYSE KOSAL BULBUL, SAEED MIAN QAISAR, AND ABDULHAMIT SUBASI13. EOG-based human-machine interaction using artificial intelligenceALBERTO LOPEZ AND FRANCISCO FERRERO14. Surface EMG-based gesture recognition using wavelet transform and ensemble learningABDULHAMIT SUBASI AND SAEED MIAN QAISAR15. EEG-based secure authentication mechanism using discrete wavelet transform and ensemble machine learning methodsABDULHAMIT SUBASI, SAEED MIAN QAISAR, AND AKILA SARIRETE16. EEG-based emotion recognition using AR burg and ensemble machine learning modelsABDULHAMIT SUBASI AND SAEED MIAN QAISAR17. Immersive virtual reality and augmented reality in human-machine interactionMUSTAFA CAN GURSESLI, ANTONIO LANATA, ANDREA GUAZZINI, AND RUCK THAWONMAS18. Mental workload levels of multiple sclerosis patients in the virtual reality environmentSEDA SASMAZ KARACAN AND HAMDI MELIH SARAOGLU19. Vision-based action recognition for the human-machine interactionANKUSH VERMA, VANDANA SINGH, AMIT PRATAP SINGH CHOUHAN, ABHISHEK, AND ANJALI RAWAT20. Security and privacy in human-machine interaction for healthcare sectorANKUSH VERMA, AMIT PRATAP SINGH CHOUHAN, VANDANA SINGH, LEKHA SINGH, GAUTAM SUKLABAIDYA, ABHISHEK SHARMA, AND PANKAJ VERMA
