Identification of Human Gripping-Force Control From Electro-Encephalographic Signals by Artificial Neural Networks

Belič, Aleš; Koritnik, Blaž; Logar, Vito; Brežan, Simon; Rutar, Veronika; Kurillo, Gregorij; Karba, Rihard; Zidar, Janez

doi:10.3182/20050703-6-cz-1902.02153

Cited by 2 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Semi-autonomous navigation systems for wheelchairs, which adapt to the patient autonomy level (Fioretti et al 2000;Seki et al 2000;Kitagawa et al 2001), or provide users with driving assistance (Yanko 1998) are an example of this approach. Moreover, it should be possible to collect signals for controlling robots in an 'intelligent home' from different sources depending on the user residual abilities (e.g., vision based signals Rao et al 2002, electro-encephalographic brain signals Belic et al 2005, or hand gestures Do et al 2005.…”

Section: Introductionmentioning

confidence: 99%

A multimode navigation system for an assistive robotics project

et al. 2008

View full text Add to dashboard Cite

Assistive technology is an emerging area, where robotic devices can help individuals with motor disabilities to achieve independence in daily activities. This paper deals with a system that provides remote control of Sony AIBO, a commercial mobile robot, within the assistive project ASPICE. The robot can be controlled by various input devices, including a Brain-Computer Interface. AIBO has been chosen for its friendly-looking aspect, in order to ease interaction with the patients. The development of the project is described by focusing on the design of the robot navigation system. Single step, semi-autonomous and autonomous navigation modes have been realized to provide different levels of control. Automatic collision avoidance is integrated in all cases. Other features of the system, such as the video feedback from the robotic platform to the user, and the use of AIBO as communication aid, are briefly described. The performance of the navigation system is shown by simulations as well as experiments. The system has been clinically validated, in order to obtain a definitive assessment through patient feedback. © 2008 Springer Science+Business Media, LLC

show abstract

Section: Introductionmentioning

confidence: 99%

A multimode navigation system for an assistive robotics project

et al. 2008

View full text Add to dashboard Cite

show abstract

“…In particular, it should be possible to collect signals for controlling devices or robots in an 'intelligent home' from different sources depending on the patient residual abilities. Recently, electroencephalographic brain signals [4], and implanted Brain Computer Interfaces [5] have been used. The systems should be validated by experiments on potential users [6].…”

Section: Introductionmentioning

confidence: 99%

Development of a multimode navigation system for an assistive robotics project

Cherubini¹,

Oriolo²,

Macrì³

et al. 2007

Proceedings 2007 IEEE International Conference on Robotics and Automation

View full text Add to dashboard Cite

Assistive technology is an emerging area where robotic devices can be used to strengthen the residual abilities of individuals with motor disabilities or to help them achieve independence in the activities of daily living. This paper deals with a project aimed at designing a system that provides remote control of home-installed appliances, including the Sony AIBO, a commercial mobile robot. The development of the project is described by focusing on the design of the robot navigation system. Single step, semi-autonomous and autonomous operating modes have been realized to provide different levels of interaction with AIBO. Automatic collision avoidance is integrated in all cases. The performance of the navigation system is shown by experiments. Moreover, the system underwent clinical validation, in order to obtain a definitive assessment through patient feedback.

show abstract

Identification of Human Gripping-Force Control From Electro-Encephalographic Signals by Artificial Neural Networks

Cited by 2 publications

References 6 publications

A multimode navigation system for an assistive robotics project

A multimode navigation system for an assistive robotics project

Development of a multimode navigation system for an assistive robotics project

Contact Info

Product

Resources

About