Myoelectric-Controlled Exoskeletal Elbow Robot to Suppress Essential Tremor: Extraction of Elbow Flexion Movement Using STFTs and TDNN
Takeshi Ando*,**, Masaki Watanabe**, Keigo Nishimoto**,
Yuya Matsumoto**, Masatoshi Seki**,
and Masakatsu G. Fujie**
*Robotics and Design for Innovative Healthcare, Graduate School of Medicine, Osaka University, 1-7 Yamada-oka, Suita, Osaka 565-0871, Japan
**Graduate School of Creative Science and Engineering, Faculty of Science and Engineering, Waseda University, 59-309, 3-4-1 Ohkubo, Shinjuku, Tokyo 169-8555, Japan
Essential tremor is the most common of all involuntary movements. Many patients with an upper-limb tremor have serious difficulties in performing daily activities. We developed a myoelectric-controlled exoskeletal robot to suppress tremor. In this article, we focus on developing a signal processing method to extract voluntary movement from a myoelectric in which the voluntary movement and tremor were mixed. First, a Low-Pass Filter (LPF) and Neural Network (NN) were used to recognize the tremor patient’s movement. Using these techniques, it was difficult to recognize the movement accurately because the myoelectric signal of the tremor patient periodically oscillated. Then, Short-Time Fourier Transformation (STFT) and NN were used to recognize the movement. This method was more suitable than LPF and NN. However, the recognition timing at the start of the movement was late. Finally, a hybrid algorithm for using both short and long windows’ STFTs, which is a kind of “mixture of experts,” was proposed and developed. With this type of signal processing, elbow flexion was accurately recognized without the time delay in starting the movement.
Yuya Matsumoto, Masatoshi Seki, and
and Masakatsu G. Fujie, “Myoelectric-Controlled Exoskeletal Elbow Robot to Suppress Essential Tremor: Extraction of Elbow Flexion Movement Using STFTs and TDNN,” J. Robot. Mechatron., Vol.24, No.1, pp. 141-149, 2012.
-  A. Anouti and W. Koller, “Tremor disorders: Diagnosis and management,” Western J. Med., Vol.162, No.6, pp. 523-530, 1998.
-  R. Elble and W. Koller, “Tremor,” Baltimore, MD: Johns Hopkins Univ. Press, 1990.
-  E. Rocon, J. Belda-Lois, J. Sanchez-Lacuesta, and J. L. Pons, “Pathological tremor management: Modelling, compensatory technology and evaluation,” Technol. Disability, Vol.16, pp. 13-18, 2004.
-  T. Ando, M. Watanabe, and M. G. Fujie, “Extraction of Voluntary Movement for an EMG Controlled Exoskeltal Robot of Tremor Patient,” Proc. of 4th Int. IEEE EMBS Conf. on Neural Engineering, pp. 120-124, 2009.
-  G. J. Alvaro, R. Eduardo, and P. J. Luis, “Estimation of Instantaneous Tremor Parameters for FES Based Tremor Suppression,” 2010 IEEE Internationa Conf. on Robotics and Automation, pp. 2922-2927, 2010.
-  E. Ohara, K. Yano, S. Horihata, T. Aoki, and Y. Nishimoto, “Tremor suppression control of Meal-Assist Robot with adaptive filter,” The 2009 IEEE ICORR, pp. 498-503, 2009.
-  E. Rocon, J. M. Belda-Lois, A. F. Ruiz, M. Manto, J. C. Moreno, and J. L. Pons, “Design and Validation of a Rehabilitation Robotic Exoskeleton for Tremor Assessment and Suppression,” IEEE TRANS. NEU. SYS. REH. ENG., Vol.15, No.3, 2007.
-  Y. Hasegawa, Y. Mikami, K. Watanabe, Z. Firouzimehr, and Y. Sankai, “Wearable handling support system for paralyzed patient,” Proc. of The 2008 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 741-746, 2008.
-  K. Kiguchi, Y. Imada, and M. Liyanage, “EMG-Based Neuro-Fuzzy Control of a 4DOF Upper-Limb Power-Assist Exoskeleton,” 29th Annual Int. Conf. of the IEEE Engineering inMedicine and Biology Society, pp. 3040-3043, 2007.
-  T. Ando, J. Okamoto, and M. G. Fujie, “Intelligent corset to support rollover of cancer bone metastasis patients,” The 2008 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 723-728, 2008.
-  M. Oosawa, “Clinical Application of Surface Electromyography,” Tokyo Women’s Medical Univ. Press, Vol.59, No.6, pp. 499-513, 1989.
-  K. Kuribayashi et al., “A discrimination system using neural network for EMG-controlled prostheses,” Proc. of IEEE Int.Workshop on Robot and Human Communication, pp. 63-68, 1992.
-  O. Fukuda, T. Tsuji et al., “An EMG controlled human supporting robot using neural network,” 1999 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 1586-1591, 1999.
-  L. Wang and T. S. Buchanan, “Prediction of joint moments using a neural network model of muscle activations from EMG signals,” IEEE Trans. Neu. Sys. Reh. Eng., Vol.10, No.1, pp. 30-37, 2002.
-  K. Kiguchi, K. Iwami, M. Yasuda, K. Watanabe, and T. Fukuda, “An exoskeletal robot for human shoulder joint motion assist,” IEEE/ASME Trans. Mech., Vol.8, No.1, pp. 125-135, 2003.
-  Y. Hou, J. M. Zurada, and W. Karwowski, “Prediction of EMG signals of trunk muscles in manual lifting using a neural network model,” IEEE Int. Conf. on Neu. Net., Vol.3, pp. 1935-1940, 2004.
-  M. Zecca et al., “Control of Multifunctional Prosthetic Hands by Electromyographic Signal,” Critical Rev. in BIO. Eng., Vol.30, No.4-6, pp. 459-485, 2002.
-  K. Hirose, “Text to read EMG,” Bunko, 1992 (in Japanese).
-  M. R. Canal, “Comparison of wavelet and short time Fourier transform methods in the analysis of EMG signals,” J. Med. Syst., Vol.34, No.1, pp. 91-94, 2010.
-  R. N. Scott, “An introduction to myoelectric prostheses,” in UNB Mongraphs on myoelectric prostheses series, A. S. Muzander (Ed.), Frederiction, N. B., Canada, Institute of Biomedical Engineering, UNB, 1984.
-  T. Onhishi, M. kazawa, and M. Tohyama, “Acoustic signal processing using multi-windowed STFT and harmonics sieving,” The fifth Int. congress on sound and vibration, pp. 2167-2174, 1997.
-  L. K. Hansen and P. Salamon, “Neural network ensembles,” IEEE Trans. Pattern Anal. Mach. Intell., 12, pp. 993-1001, 1990.
-  F. Gurgen, E. Alpaydin, and U. Uluakin, “Distributed and local neural classidiers for phoneme recognition,” Pattern Recognit. Lett. 15, pp. 1111-1118, 1994.
-  A. Sierra and C. Santa Cruz, “Global and local neural network ensembles,” Pattern Recognit. Lett., 19, pp. 651-655, 1998.
-  K. Kiguchi and T. Fukuda, “Intelligent position/force controller for industrial robot manipulators – application of fuzzy neural networks,” IEEE Trans. Ind. Electron., 44, pp. 753-761, 1997.
-  J. Tani and S. Nolfi, “Learning to perceive the world as articulated: an approach for hierarchical learning in sensory-motor systems,” Neural Networks 12, pp. 1131-1141, 1999.
-  L. Zhong, J. Lin, D. Li, T. Wang, Y. Peng, and Y. Luo, “Classification of breast tumors on ultrasound images using a hybrid neural network,” Proc. Int. Conf. on Bioinformatics and Biomedical Engineering, Wuhan, pp. 574-576, 2007.
-  Y. Yamashita and J. Tani, “Emergence of functional hierarchy in a multiple timescale neural network model: a humanoid robot experiment,” PLoS Comput. Biol., Vol.4, pp. 1-18, 2008.
-  T. Ando, J. Okamoto, and M. G. Fujie, “Micro Macro Neural Network to Recognize Rollover Movement, Advanced robotics,” Vol.25, pp. 253-271, 2011.
Copyright© 2012 by Fuji Technology Press Ltd. and Japan Society of Mechanical Engineers. All right reserved.