A waypoint-based framework in brain-controlled smart home environments: Brain interfaces, domotics, and robotics integration

The noninvasive brain-machine interface (BMI) is anticipated to be an effective tool of communication not only in laboratory settings but also in our daily livings. The direct communication channel created by BMI can assist aging societies and the handicapped and improve human welfare. In this paper we propose and experiment a BMI framework that combines BMI with a robotic house and autonomous robotic wheelchair. Autonomous navigation is achieved by placing waypoints within the house and, from the user side, the user performs BMI to give commands to the house and wheelchair. The waypoint framework can offer essential services to the user with an effectively improved information-transfer rate and is an excellent examples of the fusion of data measured by sensors in the house, which can offer insight into further studies.

[1]  Erwin Prassler,et al.  Motion coordination between a human and a mobile robot , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[2]  Gregory D. Abowd,et al.  The Aware Home: A Living Laboratory for Ubiquitous Computing Research , 1999, CoBuild.

[3]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

[4]  Cuntai Guan,et al.  Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms , 2011, IEEE Transactions on Biomedical Engineering.

[5]  Wolfgang Kastner,et al.  ThinkHome: A smart home as digital ecosystem , 2010, 4th IEEE International Conference on Digital Ecosystems and Technologies.

[6]  Christian Mandel,et al.  Navigating a smart wheelchair with a brain-computer interface interpreting steady-state visual evoked potentials , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[7]  Erik Wästlund,et al.  What you see is where you go: testing a gaze-driven power wheelchair for individuals with severe multiple disabilities , 2010, ETRA.

[8]  Christos Douligeris,et al.  Home Automation , 2008, Wiley Encyclopedia of Computer Science and Engineering.

[9]  Z J Koles,et al.  EEG source localization: implementing the spatio-temporal decomposition approach. , 1998, Electroencephalography and clinical neurophysiology.

[10]  M. Nuttin,et al.  A brain-actuated wheelchair: Asynchronous and non-invasive Brain–computer interfaces for continuous control of robots , 2008, Clinical Neurophysiology.

[11]  Antonis A. Argyros,et al.  Semi-autonomous Navigation of a Robotic Wheelchair , 2002, J. Intell. Robotic Syst..

[12]  Brice Rebsamen,et al.  A brain controlled wheelchair to navigate in familiar environments. , 2010, IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[13]  José del R. Millán,et al.  Brain-Controlled Wheelchairs: A Robotic Architecture , 2013, IEEE Robotics & Automation Magazine.

[14]  Febo Cincotti,et al.  Toward Domotic Appliances Control through a Self-paced P300-based BCI , 2011, BIOSIGNALS.

[15]  Hiroshi Ishiguro,et al.  Laser-Based Tracking of Human Position and Orientation Using Parametric Shape Modeling , 2009, Adv. Robotics.

[16]  Wolfram Burgard,et al.  The dynamic window approach to collision avoidance , 1997, IEEE Robotics Autom. Mag..

[17]  Klaus-Robert Müller,et al.  The non-invasive Berlin Brain–Computer Interface: Fast acquisition of effective performance in untrained subjects , 2007, NeuroImage.

[18]  Yoshinori Kobayashi,et al.  Robotic wheelchair based on observations of people using integrated sensors , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  Diane J. Cook,et al.  Multi-agent smart environments , 2009, J. Ambient Intell. Smart Environ..

[20]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.

[21]  Boris E. R. de Ruyter,et al.  New research perspectives on Ambient Intelligence , 2009, J. Ambient Intell. Smart Environ..

[22]  M. Nuttin,et al.  Asynchronous non-invasive brain-actuated control of an intelligent wheelchair , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[23]  Wolfram Burgard,et al.  Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters , 2007, IEEE Transactions on Robotics.

[24]  Iñaki Iturrate,et al.  A Noninvasive Brain-Actuated Wheelchair Based on a P300 Neurophysiological Protocol and Automated Navigation , 2009, IEEE Transactions on Robotics.

[25]  Wolfram Burgard,et al.  Position Estimation for Mobile Robots in Dynamic Environments , 1998, AAAI/IAAI.

[26]  Benjamin Kuipers,et al.  Integrating Multiple Representations of Spatial Knowledge for Mapping, Navigation, and Communication , 2007, Interaction Challenges for Intelligent Assistants.

[27]  Pavel Pudil,et al.  Introduction to Statistical Pattern Recognition , 2006 .