1. Simpson R, Lopresti E, Hayashi S, et al. The smart wheelchair component system. Journal of Rehabilitation Research and Development (JRRD), 2004, 41(3B): 429-442
2. Barea R, Boquete L, Mazo M, et al. System for assisted mobility using eye movements based on electrooculography. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2002,10(4): 209-218
3. Felzer T, Freisleben B. HaWCoS: The hands-free wheelchair control system. Proceedings of the 5th International ACM Conference on Assistive Technologies (Assets’02), Jul 8-10, 2002, Edinburgh, UK. New York, NY, USA: ACM, 2002: 127-134
4. Ferreira A, Silva R L, Celeste W C, et al. Human-machine interface based on muscular and brain signals applied to a robotic wheelchair. Journal of Physics: Conference Series, 2007(1): 90/012094
5. Kim K H, Kim J S, Son W, et al. A biosignal-based human interface controlling a power-wheelchair for people with motor disabilities. ETRI Journal, 2006, 28(1): 111-114
6. Yuan K. Current situation and trend of intelligent wheelchair. Journal of China Medical Devices Information, 2009, 15(1): 6,33 (in Chinese)
7. Oskoei M A, Hu H. Myoelectric control systems--A survey. Biomedical Signal Processing and Control, 2007, 2(4): 275-294
8. Wang F, Luo Z Z. Basing on AR model and BP neural network to classify sEMG. Journal of Huazhong University of Science and Technology: Nature Science Edition, 2004, 32(S1): 100-102(in Chinese)
9. Gu J. Pattern recognition of gesture based on vision and surface electromyography signal. Master Dissertation. Hefei, China: China University of Science and Technology, 2009(in Chinese)
10. Zhang K, Wang Z. Application of improved BP algorithm to surface EMG signal classification. Chinese Medical Equipment Journal, 2005, 26(12): 17-19 (in Chinese)
11. Wei L, Hu H S, Lu T, et al. Evaluating the performance of a face movement based wheelchair control interface in an indoor environment. Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO’10), Dec 14-18, 2010, Tianjin, China. Piscataway, NJ, USA: IEEE, 2010: 2387-392
12. Tsui C S L, Jia P, Gan J Q, et al. EMG-based hands-free wheelchair control with EOG attention shift detection. Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO’07), Dec 15-18, 2007, Sanya, China. Piscataway, NJ, USA: IEEE, 2007: 1266-1271
13. Wei L, Hu H S, Yuan K. Use of forehead bio-signals for controlling an intelligent wheelchair. Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO’09), Feb 22-25, 2009, Bangkok, Thailand. Piscataway, NJ, USA: IEEE, 2009: 108-113
14. Birch G E,Bozorgzadeh Z, Mason S G. Initial online evaluations of the LF-ASD brain-computer interface with able-bodied and spinal-cord subjects using imagined voluntary motor potentials. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2002, 10(4): 219-224
15. Millán J R, Mourino J. Asynchronous BCI and local neural classifiers: An overview of the adaptive brain interface project. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2003,11(2): 159-161 |