A soft robotic hand: design, analysis, sEMG control, and experiment

Soft robot is a new type of flexible robot which can imitate human hand activity. Electromyographic (EMG) signal is an important bioelectrical signal associated with muscle activity. The innovative combination of soft robot and EMG shows great potential. Based on this inspiration, a humanoid soft robotic hand controlled by EMG was proposed. We designed a single finger 3D model for the soft robotic hand and put forward the three-stage cavity structure. The finite element analysis has been performed to obtain the influence of the geometrical parameters including the number of cavities, the shape of the cavity side section, and the pressure in the cavity on the single finger bending performance. The optimal geometrical parameters were obtained. We analyzed the geometrical deformation of the finger simulation model and figured out the relationship between the input pressure of the soft hand and the angle of bending deformation. In addition, we designed and manufactured the soft robotic hand model and its pneumatic system. Twenty-four effective eigenvalues were extracted from the surface EMG signal (sEMG) of the forearm muscle group and ten-kinds-gestures recognizing system was established. Finally, we realized the online sEMG control of the soft robotic hand, so that the soft robotic hand can reproduce the gestures behavior of human. The correct rate of recognition is 96%. Conclusions obtained in this paper provide theoretical support for the development of humanoid soft robotic hand.

[1]  CianchettiMatteo,et al.  A Bioinspired Soft Robotic Gripper for Adaptable and Effective Grasping , 2015 .

[2]  J Duchêne,et al.  Variability of some SEMG parameter estimates with electrode location. , 1998, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[3]  J. Petruccelli,et al.  A threshold AR(1) model , 1984, Journal of Applied Probability.

[4]  Yangyang,et al.  Bioinspired Robotic Fingers Based on Pneumatic Actuator and 3D Printing of Smart Material , 2017 .

[5]  Mehran Jahed,et al.  A Neuro–Fuzzy Inference System for sEMG-Based Identification of Hand Motion Commands , 2011, IEEE Transactions on Industrial Electronics.

[6]  Desney S. Tan,et al.  Demonstrating the feasibility of using forearm electromyography for muscle-computer interfaces , 2008, CHI.

[7]  Markus P. Nemitz,et al.  Using Voice Coils to Actuate Modular Soft Robots: Wormbot, an Example , 2016, Soft robotics.

[8]  Desney S. Tan,et al.  Making muscle-computer interfaces more practical , 2010, CHI.

[9]  W. Blume,et al.  Neuroanatomy and neurophysiology , 1987, Oxford Handbook of Medical Sciences.

[10]  Abdulhamit Subasi,et al.  EEG signal classification using PCA, ICA, LDA and support vector machines , 2010, Expert Syst. Appl..

[11]  Kevin B. Englehart,et al.  A wavelet-based continuous classification scheme for multifunction myoelectric control , 2001, IEEE Transactions on Biomedical Engineering.

[12]  Liu Zheng-wei FEA of hyperelastic rubber material based on Mooney-Rivlin model and Yeoh model , 2008 .

[13]  Kongqiao Wang,et al.  Hand gesture recognition and virtual game control based on 3D accelerometer and EMG sensors , 2009, IUI.

[14]  Kongqiao Wang,et al.  Study on Online Gesture sEMG Recognition , 2007, ICIC.

[15]  Oliver Brock,et al.  A novel type of compliant and underactuated robotic hand for dexterous grasping , 2016, Int. J. Robotics Res..

[16]  Kaspar Althoefer,et al.  Soft and Stretchable Sensor Using Biocompatible Electrodes and Liquid for Medical Applications , 2015, Soft robotics.

[17]  John R. Anderson,et al.  The discovery of processing stages: Analyzing EEG data with hidden semi-Markov models , 2015, NeuroImage.

[18]  S. Lark,et al.  Electromyographic analysis of muscle activation during pull-up variations. , 2017, Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology.

[19]  Fionnuala Connolly,et al.  Automatic design of fiber-reinforced soft actuators for trajectory matching , 2016, Proceedings of the National Academy of Sciences.

[20]  Robert J. Wood,et al.  Soft robotic glove for combined assistance and at-home rehabilitation , 2015, Robotics Auton. Syst..

[21]  Rebecca K. Kramer,et al.  Soft Material Characterization for Robotic Applications , 2015 .

[22]  George M. Whitesides,et al.  Towards a soft pneumatic glove for hand rehabilitation , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[23]  D. Rus,et al.  Design, fabrication and control of soft robots , 2015, Nature.

[24]  Li Yang,et al.  Feature extraction and classification of sEMG based on ICA and EMD decomposition of AR model , 2011, 2011 International Conference on Electronics, Communications and Control (ICECC).

[25]  Stuart Donaldson,et al.  SEMG Evaluations: An Overview , 2003, Applied psychophysiology and biofeedback.

[26]  J. Rogers,et al.  Stretchable Electronics: Materials Strategies and Devices , 2008 .

[27]  Mehmet Remzi Dogar,et al.  Haptic identification of objects using a modular soft robotic gripper , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[28]  Blake Hannaford,et al.  McKibben artificial muscles: pneumatic actuators with biomechanical intelligence , 1999, 1999 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (Cat. No.99TH8399).

[29]  Wang Jian sEMG Signal Analysis Method and Its Application Research , 2000 .

[30]  Zhang Yan,et al.  A NOVEL FACE RECOGNITION METHOD BASED ON LINEAR DISCRIMINANT ANALYSIS , 2003 .

[31]  S Micera,et al.  A hybrid approach to EMG pattern analysis for classification of arm movements using statistical and fuzzy techniques. , 1999, Medical engineering & physics.

[32]  Markus P. Nemitz,et al.  Controlling and Simulating Soft Robotic Systems: Insights from a Thermodynamic Perspective , 2016 .

[33]  Robert J. Wood,et al.  Mechanically programmable bend radius for fiber-reinforced soft actuators , 2013, 2013 16th International Conference on Advanced Robotics (ICAR).

[34]  Dmitry Berenson,et al.  Improving Soft Pneumatic Actuator fingers through integration of soft sensors, position and force control, and rigid fingernails , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).