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A BCI-controlled robotic assistant for quadriplegic people in domestic and professional life

Published online by Cambridge University Press:  13 July 2011

Sorin M. Grigorescu ,
Thorsten Lüth ,
Christos Fragkopoulos ,
Marco Cyriacks  and
Axel Gräser
Sorin M. Grigorescu*
Affiliation:
Department of Automation, Transilvania University of Braşov, Mihai Viteazu 5, 500174, Braşov, Romania. E-mail: [email protected]
Thorsten Lüth
Affiliation:
Institute of Automation, University of Bremen, NW1/FB1 Otto-Hahn-Allee 1, 28359 Bremen, Germany. E-mail: [email protected], [email protected], [email protected], [email protected]
Christos Fragkopoulos
Affiliation:
Institute of Automation, University of Bremen, NW1/FB1 Otto-Hahn-Allee 1, 28359 Bremen, Germany. E-mail: [email protected], [email protected], [email protected], [email protected]
Marco Cyriacks
Affiliation:
Institute of Automation, University of Bremen, NW1/FB1 Otto-Hahn-Allee 1, 28359 Bremen, Germany. E-mail: [email protected], [email protected], [email protected], [email protected]
Axel Gräser
Affiliation:
Institute of Automation, University of Bremen, NW1/FB1 Otto-Hahn-Allee 1, 28359 Bremen, Germany. E-mail: [email protected], [email protected], [email protected], [email protected]
*
*Corresponding author. email: [email protected]
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Summary

In this paper, a Brain–Computer Interface (BCI) control approach for the assistive robotic system FRIEND is presented. The objective of the robot is to assist elderly and persons with disabilities in their daily and professional life activities. FRIEND is presented here from an architectural point of view, that is, as an overall robotic device that includes many subareas of research, such as human–robot interaction, perception, object manipulation and path planning, robotic safety, and so on. The integration of the hardware and software components is described relative to the interconnections between the various elements of FRIEND and the approach used for human–machine interaction. Since the robotic system is intended to be used especially by patients suffering from a high degree of disability (e.g., patients which are quadriplegic, have muscle diseases or serious paralysis due to strokes, or any other diseases with similar consequences for their independence), an alternative non-invasive BCI has been investigated. The FRIEND–BCI paradigm is explained within the overall structure of the robot. The capabilities of the robotic system are demonstrated in three support scenarios, one that deals with Activities of daily living (ADL) and two that are taking place in a rehabilitation workshop. The proposed robot was clinically evaluated through different tests that directly measure task execution time and hardware performance, as well as the acceptance of robot by end-users.

Keywords

Assistive roboticsWheelchair-mounted manipulatorsBrain–computer interfaces
Type
Articles
Information
Robotica , Volume 30 , Issue 3 , May 2012 , pp. 419 - 431
Copyright
Copyright © Cambridge University Press 2011

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References

1.Van der Loos, M. H. and Reinkensmeyer, D. J., “Rehabilitation and Health Care Robotics,” In: Springer Handbook of Robotics (Siciliano, B. and Khatib, O., eds.) (Springer-Verlag, Berlin, Heidelberg, Germany, May 2007) pp. 1223–1252.Google Scholar
2.Van der Loos, M. H., Mahoney, R. and Ammi, C., “Great Expectations for Rehabilitation Mechatronics in the Comming Decade,” In: Advances in Rehabilitation Robotics, Lect. Notes (Bien, Z. and Stefanov, D., eds.) (Springer-Verlag, Berlin, Heidelberg, Germany, 2004) pp. 427–433.Google Scholar
3.Lange, C., “Blickgesteuerte Interaktion mit Peripheriegeraeten – Technische Loesung und ergonomische Absicherung,” In: Mensch und Maschine: wie Brain-Computer-Interfaces und andere Innovationen gelaehmten Menschen kommunizieren helfen (Pantke, K.-H., ed) (Springer-Verlag, Berlin, Heidelberg, Germany, 2010) pp. 163–181.Google Scholar
4.Cousins, S., Gerkey, B., Conley, K. and Garage, W., “Sharing software with ROS [ROS topics],” IEEE Rob. Autom. Mag. 17 (2), 12–14 (Jun. 2010).CrossRefGoogle Scholar
5.Reiser, U., Connette, C., Fischer, J., Kubacki, J., Bubeck, A., Weisshardt, F., Jacobs, T., Parlitz, C., Haegele, M. and Verl, A., “Care-O-Bot 3 – Creating a Product Vision for Service Robot Applications by Integrating Design and Technology,” Proceedings of the 2009 International Conference on Intelligent Robots and Systems, St. Louis, MO, USA, Oct. 2009.Google Scholar
6.Martens, C., Ruchel, N., Lang, O., Ivlev, O. and Gräser, A., “A FRIEND for assisting handicapped people,” IEEE Rob. Autom. Mag. 8 (1)57–65 (Mar. 2001).CrossRefGoogle Scholar
7.Prenzel, O., Martens, C., Cyriacks, M., Wang, C. and Gräser, A., “System controlled user interaction within the service robotic control architecture MASSiVE,” Robotica (Special issue) 25 (2)237–244 (Mar. 2007).Google Scholar
8.Allison, B., Wolpaw, E. and Wolpaw, J., “Brain-computer interface systems: progress and prospects,” Expert Rev. Med. Devices 4 (4)463–474 (2007).Google Scholar
9.Kübler, A., Nijboer, F., Mellinger, J., Vaughan, T., Pawelzik, H., Schalk, G., McFarland, D., Birbaumer, N. and Wolpaw, J., “Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface,” Neurology 64, 1775–1777 (2005).CrossRefGoogle ScholarPubMed
10.Simpson, R. C., “Smart wheelchairs: A literature review,” Rehabil. Res. Dev. 42 (4), 423–436 (Aug. 2005).CrossRefGoogle ScholarPubMed
11.Topping, M., “Handy 1, A Robotic-Arm to Aid Independence for Severely Disabled People,” In: Integration of Assistive Technology in the Information Age (Mokhtari, M., ed.) (IOS Press, Netherlands, 2001) pp. 142–147.Google Scholar
12.Mokhtari, M. and Amni, C., “Assistive Technology for the Disabled People: Should it Work? The French Approach,” Proceedings of the 2nd International Workshop on Human-friendly Welfare Robotic Systems, KAIST, Daejeon, South Korea (Jan. 2001).Google Scholar
13.De Witte, L., Goossens, M., Wessels, R., Van der Pijl, D., Gelderblom, G., Van Hoofd, W., Tilli, D., Dijcks, B. and Van Soest, K., “Cost-Effectiveness of Specialized Assistive Technology: the MANUS Robot Manipulator,” In: Annual International Society of Technology Assessment in Health Care Meeting, 16 (2000), 284 pp.Google Scholar
14.Tsui, K. M. and Yanco, H. A., “Human-in-the-Loop Control of an Assistive Robot Arm,” Proceedings of the Workshop on Manipulation for Human Environments, Robotics: Science and Systems Conference, Philadelphia, PA, USA (Aug. 19, 2006).Google Scholar
15.Bien, Z., Chung, M.-J., Chang, P.-H., Kwon, D.-S., Kim, D.-J., Han, J.-S., Kim, J.-H., Kim, D.-H., Park, H.-S., Kang, S.-H., Lee, K. and Lim, S.-C., “Integration of a rehabilitation robotic system (KARES II) with human-friendly man-machine interaction units,” Auton. Rob. 16 (2), 165–191, (Nov. 2004).Google Scholar
16.Dune, C., Leroux, C. and Marchand, E., “Intuitive Human Interaction with an Arm Robot for Severely Handicapped People – A One Click Approach,” Proceeding of the IEEE 10th International Conference on Rehabilitation Robotics ICORR 2007, Noordwijk, Netherlands (Jun. 2007).Google Scholar
17.Grigorescu, S. M., “Robust Machine Vision for Service Robotics,” Ph.D. dissertation (Institute of Automation, Bremen University, Bremen, Germany, Jun. 2010).Google Scholar
18.Fragkopoulos, C. and Gräser, A., “A RRT-Based Path Planning Algorithm for Rehabilitation Robots,” Proceedings of the 41st ISR/Robotics 2010, Munich 2010, Munich, Germany (Jun. 2010).Google Scholar
19.Prenzel, O., “Process Model for the Development of Semi-Autonomous Service Robots,” Ph.D. dissertation (Institute of Automation, Bremen University, Bremen, Germany, Jul. 2009).Google Scholar
20.Valbuena, D., Cyriacks, M., Friman, O., Volosyak, I. and Gräser, A., “Brain-Computer Interface for High-Level Control of Rehabilitation Robotic Systems,” In: Proceedings of IEEE ICORR'07 (Jun. 2007) pp. 619–625.Google Scholar
21.Donchin, E., Spencer, K. and Wijesinghe, R., “The mental prosthesis: Assessing the speed of a P300-based brain-computer interface,” IEEE Trans. Rehabil. Eng. 8 (2), 174–179 (Jun. 2000).CrossRefGoogle ScholarPubMed
22.Edlinger, G., Holzner, C., Guger, C., Groenegress, C. and Slater, M., “Brain-Computer Interfaces for Goal-Orientated Control of a Virtual Smart Home Environment,” In: Proceedings of the 4th International IEEE/EMBS Conference on on Neural Engineering NER 09 (May 2009) pp. 463–465.CrossRefGoogle Scholar
23.Rebsamen, B., Guan, C., Zhang, H., Wang, C., Teo, C., Ang, M. H. Jr. and Burdet, E., “A brain controlled wheelchair to navigate in familiar environments,” IEEE Trans. Neural Syst. Rehabil. Eng. 18, 590–598 (Dec. 2010), PMID: 20460212.Google Scholar
24.Bell, C. J., Shenoy, P., Chalodhorn, R. and Rao, R. P. N., “Control of a humanoid robot by a noninvasive brain-computer interface in humans,” J. Neural Eng. 5 (2), 214–220 (2008).CrossRefGoogle ScholarPubMed
25.Teymourian, A., Lueth, T., Gräser, A., Felzer, T. and Nordmann, R., “Brain-Controlled Finite State Machine for Wheelchair Navigation,” In: Assets '08: Proceedings of the 10th International ACM SIGACCESS Conference on Computers and Accessibility, New York, NY, USA (2008) pp. 257–258.Google Scholar
26.Tangermann, M., Krauledat, M., Grzeska, K., Sagebaum, M., Vidaurre, C., Blankertz, B. and Müller, K.-R., “Playing Pinball with Non-Invasive BCI,” Proceedings of the Twenty-Third Annual Conference on Neural Information Processing Systems (NIPS), Vancouver, BC, Canada (2009).Google Scholar
27.Tanaka, K., Matsunaga, K. and Wang, H. O., “Electroencephalogram-based control of an electric wheelchair,” IEEE Trans. Robot. 21 (4), 762–766 (Aug. 2005).CrossRefGoogle Scholar
28.Mandel, C., Lüth, T., Laue, T., Röfer, T., Gräser, A. and Krieg-Brückner, B., “Navigating a Smart Wheelchair with a Brain-Computer Interface Interpreting Steady-State Visual Evoked Potentials,” In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (Oct. 2009) pp. 1118–1125.Google Scholar
29.Lebedev, M. A. and Nicolelis, M. A. L., “Brain-machine interfaces: Past, present and future,” Trends Neurosci. 29 (9)536–546 (Sep. 2006).CrossRefGoogle ScholarPubMed
30.Lueth, T., Ojdanic, D., Friman, O., Prenzel, O. and Gräser, A., “Low Level Control in a Semi-Autonomous Rehabilitation Robotic System via a Brain-Computer Interface,” Proceedings of the IEEE 10th International Conference on Rehabilitation Robotics ICORR 2007, Noordwijk, Netherlands (Jun. 2007).Google Scholar
31.Neuper, C., Müller-Putz, G., Scherer, R. and Pfurtscheller, G., “Motor imagery and EEG-based control of spelling devices and neuroprostheses,” Prog. Brain Res. 159, 393–409 (2006).Google Scholar
32.Wang, Y., Gao, X., Hong, B., Jia, C. and Gao, S., “Brain-computer interfaces based on visual evoked potentials,” IEEE Eng. Med. Biol. Mag. 27 (5), 64–71 (2008).Google Scholar
33.Burkitt, G., Silberstein, R., Cadusch, P. and Wood, A., “Steady-state visual -evoked potentials and travelling waves,” Clin. Neurophysiol. 111, 246–258 (2000).CrossRefGoogle ScholarPubMed
34.Gao, X., Xu, X., Cheng, M. and Gao, S., “A BCI-based environmental controller for the motion-disabled,” IEEE Trans. Neural Syst. Rehabil. Eng. 11 (2), 137–140 (Jun. 2003).Google Scholar
35.Friman, O., Volosyak, I. and Gräser, A., “Multiple channel detection of steady-state visual evoked potentials for brain-computer interfaces,” IEEE Trans. Biomed. Eng. 54 (4), 742–750 (Apr. 2007).CrossRefGoogle ScholarPubMed
36.Allison, B., Lüth, T., Valbuena, D., Teymourian, A., Volosyak, I. and Gräser, A., “BCI demographics: How many (and what kinds of) people can use an SSVEP BCI?” IEEE Trans. Neural Syst. Rehabil. Eng. 18 (2), 107–116 (Apr. 2010).Google Scholar
37.Volosyak, I., Cecotti, H. and Gräser, A., “Steady-State Visual Evoked Potential Response – Impact of the Time Segment Length,” In: Proceedings of the 7th International Conference on Biomedical Engineering, BioMed 2010, Innsbruck, Austria (Feb. 17–19, 2010) pp. 288–292.Google Scholar
38.Bicchi, A., Peshkin, M. A. and Colgate, J. E., “Safety for Physical Human-Robot Interaction,” In: Springer Handbook of Robotics (Siciliano, B. and Khatib, O., eds.) (Springer-Verlag, Berlin, Heidelberg, Germany, May 2007).Google Scholar
39.Tsui, K. M., Feil-Seifer, D. J., Mataric, M. J. and Yanco, H. A., “Performance Evaluation Methods for Assistive Robotic Technology,” In: Performance Evaluation and Benchmarking of Intelligent Systems (Madhavan, R., Tunstel, E., and Messina, E., eds.) (Springer, New York, 2009) pp. 41–66.Google Scholar
40.Volosyak, I., Cecotti, H., Valbuena, D. and Gräser, A., “Evaluation of the Bremen SSVEP-Based BCI in Real World Conditions,” Proceedings of IEEE ICORR'09 (Jun. 2009) pp. 322–331.Google Scholar