Continuous estimation of hand's joint angles from sEMG using wavelet-based features and SVR

Developing robust hand kinematic estimation mechanisms is considered an essential requirement to enhance the quality of life for amputees. These robust control mechanisms enable to control robotic hands in a way that can mimic the human hand functions. In this paper, we propose a surface electromyography (sEMG)-based approach for continuous estimation of wrist and fingers' joint angles. The proposed approach utilizes the discrete wavelet transform (DWT) to construct a time-frequency representation of the sEMG signals. Then, using the time-frequency representation, a set of time-frequency features are extracted. In order to estimate the wrist and fingers' joint angles, we utilize the extracted time-frequency features to train a set of support vector regression (SVR) models. Evaluation results of the proposed approach, using the NinaPro database, demonstrate the efficiency of the approach in providing a feasible method towards accurately estimating wrist and fingers' joint angles from the sEMG signals.

[1]  Ilja Kuzborskij,et al.  Characterization of a Benchmark Database for Myoelectric Movement Classification , 2015, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[2]  Daniel R Merrill,et al.  Development of an implantable myoelectric sensor for advanced prosthesis control. , 2011, Artificial organs.

[3]  Kathryn Ziegler-Graham,et al.  Estimating the prevalence of limb loss in the United States: 2005 to 2050. , 2008, Archives of physical medicine and rehabilitation.

[4]  Ali N. Akansu,et al.  Chapter 2 – Orthogonal Transforms , 1992 .

[5]  Erik J. Scheme,et al.  Support Vector Regression for Improved Real-Time, Simultaneous Myoelectric Control , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[6]  R. Haddad,et al.  Multiresolution Signal Decomposition: Transforms, Subbands, and Wavelets , 1992 .

[7]  References , 1971 .

[8]  Tomohiro Shibata,et al.  Fast Reinforcement Learning for Three-Dimensional Kinetic Human–Robot Cooperation with an EMG-to-Activation Model , 2011, Adv. Robotics.

[9]  Dario Farina,et al.  EMG-based simultaneous and proportional estimation of wrist/hand kinematics in uni-lateral trans-radial amputees , 2011, Journal of NeuroEngineering and Rehabilitation.

[10]  Silvestro Micera,et al.  EMG-based learning approach for estimating wrist motion , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[11]  C. Jang,et al.  A Survey on Activities of Daily Living and Occupations of Upper Extremity Amputees , 2011, Annals of rehabilitation medicine.

[12]  H. Kawasaki,et al.  Estimation of finger joint angles from sEMG using a recurrent neural network with time-delayed input vectors , 2009, 2009 IEEE International Conference on Rehabilitation Robotics.

[13]  Guido Bugmann,et al.  Classification of Finger Movements for the Dexterous Hand Prosthesis Control With Surface Electromyography , 2013, IEEE Journal of Biomedical and Health Informatics.

[14]  Manfredo Atzori,et al.  Electromyography data for non-invasive naturally-controlled robotic hand prostheses , 2014, Scientific Data.

[15]  Nitish V. Thakor,et al.  Decoding of Individuated Finger Movements Using Surface Electromyography , 2009, IEEE Transactions on Biomedical Engineering.

[16]  Nitish V. Thakor,et al.  Continuous decoding of finger position from surface EMG signals for the control of powered prostheses , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[17]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.