Galileo bionic hand: sEMG activated approaches for a multifunction upper-limb prosthetic

Surface electromyography (sEMG) commonly used in upper-limb prostheses requires expensive medical equipment to get accurate results, and even then only a few actions can be classified. We propose an sEMG activated embedded system based on Digital Signal Processing and Machine Learning, to interpret the user intention with the purpose of controlling a low-cost 3D printed hand prosthesis with multiple Degrees of Freedom (DOF). The system has three different operating modes with a user-friendly Human Machine Interface (HMI), in order to increase the amount of customized hand postures that can be performed by the user, providing functionalities that fit on their daily chores and allowing to use inexpensive surface mounted passive electrodes in order to keep a low cost approach. Inasmuch as sEMG activation allows the user to consciously perform the desired action, on the other hand a touchscreen enables the possibility to select different predefined actions and operating modes, as well as provide necessary visual feedback. Moreover, in another operating mode, a speech recognition module recognizes user speech in 3 different languages, allowing the user more sEMG activated postures. Finally, an operating mode based on Artificial Neural Networks (ANN) classifies 5 hand gestures that can be easily accomplished by below elbow amputees. The system was tested and obtained high accuracy and great responsiveness on the different modes of operation.

[1]  Luca Benini,et al.  EMG-based hand gesture recognition with flexible analog front end , 2014, 2014 IEEE Biomedical Circuits and Systems Conference (BioCAS) Proceedings.

[2]  F. Mohd-Yasin,et al.  Techniques of EMG signal analysis: detection, processing, classification and applications , 2006, Biological Procedures Online.

[3]  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.

[4]  Surya P. N. Singh,et al.  V-REP: A versatile and scalable robot simulation framework , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  R.N. Scott,et al.  A new strategy for multifunction myoelectric control , 1993, IEEE Transactions on Biomedical Engineering.

[6]  S. N. Sidek,et al.  Mapping of EMG signal to hand grip force at varying wrist angles , 2012, 2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences.

[7]  Han-Pang Huang,et al.  DSP-based controller for a multi-degree prosthetic hand , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[8]  Han-Pang Huang,et al.  Development of a myoelectric discrimination system for a multi-degree prosthetic hand , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[9]  Luca Benini,et al.  Hybrid EMG classifier based on HMM and SVM for hand gesture recognition in prosthetics , 2015, 2015 IEEE International Conference on Industrial Technology (ICIT).

[10]  Stefano Stramigioli,et al.  Myoelectric forearm prostheses: state of the art from a user-centered perspective. , 2011, Journal of rehabilitation research and development.

[11]  Qiang Li,et al.  A Hand Gesture Recognition Framework and Wearable Gesture-Based Interaction Prototype for Mobile Devices , 2014, IEEE Transactions on Human-Machine Systems.

[12]  Paul Kaufmann,et al.  Investigating evolvable hardware classification for the BioSleeve electromyographic interface , 2013, 2013 IEEE International Conference on Evolvable Systems (ICES).

[13]  Guanglin Li,et al.  Selection of sampling rate for EMG pattern recognition based prosthesis control , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[14]  Martin Fodslette Meiller.  A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning , 1993 .

[15]  Tzyy-Ping Jung,et al.  Dry-Contact and Noncontact Biopotential Electrodes: Methodological Review , 2010, IEEE Reviews in Biomedical Engineering.

[16]  Carlo J. De Luca,et al.  The Use of Surface Electromyography in Biomechanics , 1997 .

[17]  G. Gini,et al.  Classification of EMG signals through wavelet analysis and neural networks for controlling an active hand prosthesis , 2007, 2007 IEEE 10th International Conference on Rehabilitation Robotics.