The weight load inconsistency effect on voluntary movement recognition of essential tremor patient

Essential Tremor (ET) refers to involuntary movements of a part of the body. ET patients have serious difficulties in performing their daily living activities. Our ultimate goal is to develop a system that can enable ET patients to perform their daily living activities. We are in the process of developing an exoskeletal robot for ET patients. This robot is controlled by estimation of voluntary movement using surface electromyogram (EMG) signal input and a Neural Network (NN) learning algorithm. However, the EMG signal of ET patients contains not only signals from voluntary movements but also noise from involuntary tremors. We have therefore developed a signal processing method to suppress tremor noise present in the surface EMG signal. The proposed filter is based on the hypothesis that tremor noise can be approximated to powered sine wave. It have been confirmed that the proposed filter increases the accuracy of recognition. In this paper, we have focused on the effect of inconsistency of weight load between instruction signal and input signal. When the instruction signal comprised unloaded motion, our voluntary movement estimation method worked stably with the loaded motion's EMG input.

[1]  Ken'ichi Yano,et al.  Tremor suppression control of Meal-Assist Robot with adaptive filter , 2009, 2009 IEEE International Conference on Rehabilitation Robotics.

[2]  Takeshi Ando,et al.  Filtering Essential Tremor noise on surface EMG based on squared sine wave approximation , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  Takeshi Ando,et al.  Development of robotic upper limb orthosis with tremor suppressiblity and elbow joint movability , 2011, 2011 IEEE International Conference on Systems, Man, and Cybernetics.

[4]  R.N. Scott,et al.  The application of neural networks to myoelectric signal analysis: a preliminary study , 1990, IEEE Transactions on Biomedical Engineering.

[5]  Ken'ichi Yano,et al.  Development of tremor-suppression filter for meal-assist robot , 2009, World Haptics 2009 - Third Joint EuroHaptics conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems.

[6]  Toshio Tsuji,et al.  An EMG controlled human supporting robot using neural network , 1999, Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289).

[7]  H. L. Journée,et al.  Demodulation of Amplitude Modulated Noise: A Mathematical Evaluation of a Demodulator for Pathological Tremor EMG's , 1983, IEEE Transactions on Biomedical Engineering.

[8]  Cameron N. Riviere,et al.  Toward active tremor canceling in handheld microsurgical instruments , 2003, IEEE Trans. Robotics Autom..

[9]  Toshio Tsuji,et al.  A log-linearized Gaussian mixture network and its application to EEG pattern classification , 1999, IEEE Trans. Syst. Man Cybern. Part C.

[10]  Eduardo Rocon,et al.  Pathological tremor management: Modelling, compensatory technology and evaluation , 2004 .

[11]  Geoffrey E. Hinton,et al.  Phoneme recognition using time-delay neural networks , 1989, IEEE Trans. Acoust. Speech Signal Process..

[12]  Takeshi Ando,et al.  Extraction of voluntary movement for an EMG controlled exoskeltal robot of tremor patients , 2009, 2009 4th International IEEE/EMBS Conference on Neural Engineering.

[13]  K. Kuribayashi,et al.  A discrimination system using neural network for EMG-controlled prostheses , 1992, [1992] Proceedings IEEE International Workshop on Robot and Human Communication.

[14]  Richard M. Stern,et al.  Efficient Cepstral Normalization for Robust Speech Recognition , 1993, HLT.