Human-Computer Interface Technologies for the Motor Impaired

Introduction
Abstract
Introduction: Human–computer interface for people with disabilities
Background
History
Future of HCI
Layout of the book
Reference

Human–computer interface: Mechanical sensors
Abstract
Introduction
Modified devices
Sensors
Applications of HCI based on mechanical sensors
Current research and future improvements
References

Brain–computer interface based on thought waves
Abstract
Introduction
History of brain–computer interface
Significance of BCI devices
BCI technology
System design
Signal analysis
BCI translation algorithms
User consideration
Applications of BCI
Limitations
Future research
Ethical consideration
References

Evoked potentials-based brain–computer interface
Abstract
Introduction
Brain–computer interface (BCI) systems based on steady-state visual evoke potential
Design challenges and limitations
Results
User benefits and improvements
References

Myoelectric-based hand gesture recognition for human–computer interface applications
Abstract
Introduction
Background
Current technologies and implementation
References

Video-based hand movement for human–computer interface
Abstract
Introduction
Background
Data analysis
Discussion
User requirements
User benefits
Shortcomings
Future developments
References

Human–computer interface based on electrooculography
Abstract
Introduction
Background
Current technologies: Historical to state of the art
Example of EOG-based system
Results
Discussion
Limitations of the study
Discussion: User benefits and limitations
References
Further reading

Video-based eye tracking
Abstract
Introduction
Background and history
An example eye-tracking method
Data analysis
Results
Discussion: User benefits and limitations
References

Speech for controlling computers
Abstract
Introduction
History of speech-based machine commands
Automatic speech recognition (ASR)
Speech denoising methods
Speech analysis fundamentals
Subsections of speech: Phonemes
How people speak: Speech production model
Place principle hearing model
Features selection for speech analysis
Speech feature classification
Artificial neural networks
Limitations in current systems
References

Lip movement for human–computer interface
Abstract
Introduction: History and applications
Current technologies
User requirements
Example of voiceless speech recognition systems
Discussion: User benefits
Summary
References

Biography

Dinesh K. Kumar received a B.Tech from IIT Madras, and a Ph.D in biomedical engineering from IIT Delhi and AIIMS, Delhi. He is a professor and leader of biomedical engineering at RMIT University, Melbourne, Australia. Dr. Kumar has published more than 330 refereed papers in the field, and his interests include muscle control, affordable diagnostics, and human–computer interface. He is editor of multiple journals, chairs a range of conferences related to biomedical engineering, and enjoys walking in nature in his spare time.

Sridhar Poosapadi Arjunan received a B.Eng in electronics and communication from the University of Madras, India; a M.Eng in communication systems from Madurai Kamaraj University, India; and a Ph.D in biomedical signal processing from RMIT University, Australia. He is currently a postdoctoral research fellow with Biosignals Lab at RMIT University. Dr. Poosapadi Arjunan is a recipient of the RMIT SECE Research Scholarship, CASS Australian Early Career Researcher Grant, and the Australia-India ECR Fellowship. His major research interests include biomedical signal processing, rehabilitation study, fractal theory, and human–computer interface applications.