A Soft Exoglove Equipped With a Wearable Muscle-Machine Interface Based on Forcemyography and Electromyography

Soft, lightweight, underactuated assistive gloves (exogloves) can be useful for enhancing the capabilities of a healthy individual or to assist the rehabilitation of patients who suffer from conditions that limit the mobility of their fingers. However, most solutions found in the literature do not offer individual control of the fingers, hindering the execution of different types of grasps. In this letter, we focus on the development of a soft, underactuated, tendon-driven exo-glove that is equipped with a muscle-machine interface combining Electromyography and Forcemyography sensors to decode the user intent and allow the execution of specific grasp types. The device is experimentally tested and evaluated using different types of experiments: first, grasp experiments to assess the capability of the proposed muscle machine interface to discriminate between different grasp types and second, force exertion capability experiments, which evaluate the maximum forces that can be applied for different grasp types. The proposed device weighs 1150 g and costs $\sim$ 1000 USD (in parts). The exoglove is capable of considerably improving the grasping capabilities of the user, facilitating the execution of different types of grasps and exerting forces up to 20 N.

[1]  Niels Smaby,et al.  Identification of key pinch forces required to complete functional tasks. , 2004, Journal of rehabilitation research and development.

[2]  Carlo Menon,et al.  Continuous Prediction of Finger Movements Using Force Myography , 2016 .

[3]  Panagiotis K. Artemiadis,et al.  Quantifying anthropomorphism of robot hands , 2013, 2013 IEEE International Conference on Robotics and Automation.

[4]  Alessandro Chiolerio,et al.  Inkjet printed flexible electrodes for surface electromyography , 2015 .

[5]  Yu Liu,et al.  Fabrication of Copper Electrode on Flexible Substrate Through Ag+-Based Inkjet Printing and Rapid Electroless Metallization , 2017, IEEE Transactions on Components, Packaging and Manufacturing Technology.

[6]  Panagiotis K. Artemiadis,et al.  A Learning Scheme for Reach to Grasp Movements: On EMG-Based Interfaces Using Task Specific Motion Decoding Models , 2013, IEEE Journal of Biomedical and Health Informatics.

[7]  Dingguo Zhang,et al.  Soft robotic glove with integrated sEMG sensing for disabled people with hand paralysis , 2016, 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[8]  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.

[9]  Jee-Hwan Ryu,et al.  Portable Exoskeleton Glove With Soft Structure for Hand Assistance in Activities of Daily Living , 2017, IEEE/ASME Transactions on Mechatronics.

[10]  Nitish V. Thakor,et al.  Stable force-myographic control of a prosthetic hand using incremental learning , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[11]  Carlo Menon,et al.  Exploration of Force Myography and surface Electromyography in hand gesture classification. , 2017, Medical engineering & physics.

[12]  Edward A. Clancy,et al.  A soft robotic exomusculature glove with integrated sEMG sensing for hand rehabilitation , 2013, 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR).

[13]  S. Leonhardt,et al.  A survey on robotic devices for upper limb rehabilitation , 2014, Journal of NeuroEngineering and Rehabilitation.

[14]  Mikhail Kuznetsov,et al.  Filtering the surface EMG signal: Movement artifact and baseline noise contamination. , 2010, Journal of biomechanics.

[15]  Minas V. Liarokapis,et al.  On Muscle Selection for EMG Based Decoding of Dexterous, In-Hand Manipulation Motions , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[16]  Minas Liarokapis,et al.  On the Development of Adaptive, Tendon-Driven, Wearable Exo-Gloves for Grasping Capabilities Enhancement , 2019, IEEE Robotics and Automation Letters.

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

[18]  Rita M Patterson,et al.  Soft robotic devices for hand rehabilitation and assistance: a narrative review , 2018, Journal of NeuroEngineering and Rehabilitation.

[19]  Hong Kai Yap,et al.  Design and Preliminary Feasibility Study of a Soft Robotic Glove for Hand Function Assistance in Stroke Survivors , 2017, Front. Neurosci..

[20]  Carlo Menon,et al.  Towards the development of a wearable feedback system for monitoring the activities of the upper-extremities , 2014, Journal of NeuroEngineering and Rehabilitation.

[21]  Brian Byunghyun Kang,et al.  Development of a polymer-based tendon-driven wearable robotic hand , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[22]  Simon Ferguson,et al.  Grasp Recognition From Myoelectric Signals , 2002 .

[23]  Carlo Menon,et al.  Force Myography to Control Robotic Upper Extremity Prostheses: A Feasibility Study , 2016, Front. Bioeng. Biotechnol..

[24]  Paul Lukowicz,et al.  Sensing muscle activities with body-worn sensors , 2006, International Workshop on Wearable and Implantable Body Sensor Networks (BSN'06).

[25]  Hong Kai Yap,et al.  A fabric-regulated soft robotic glove with user intent detection using EMG and RFID for hand assistive application , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[26]  Minas V. Liarokapis,et al.  EMG Based Decoding of Object Motion in Dexterous, In-Hand Manipulation Tasks , 2018, 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob).

[27]  C. Burgar,et al.  Robot-assisted movement training compared with conventional therapy techniques for the rehabilitation of upper-limb motor function after stroke. , 2002, Archives of physical medicine and rehabilitation.

[28]  Patrick van der Smagt,et al.  Learning EMG control of a robotic hand: towards active prostheses , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[29]  Siddhartha S. Srinivasa,et al.  Benchmarking in Manipulation Research: Using the Yale-CMU-Berkeley Object and Model Set , 2015, IEEE Robotics & Automation Magazine.

[30]  G. Jabbour,et al.  Inkjet Printing—Process and Its Applications , 2010, Advanced materials.

[31]  Marco Santello,et al.  Proof of Concept of an Online EMG-Based Decoding of Hand Postures and Individual Digit Forces for Prosthetic Hand Control , 2017, Front. Neurol..

[32]  Tamara Grujic Supuk,et al.  Design, Development and Testing of a Low-Cost sEMG System and Its Use in Recording Muscle Activity in Human Gait , 2014, Sensors.

[33]  Mark R. Cutkosky,et al.  On grasp choice, grasp models, and the design of hands for manufacturing tasks , 1989, IEEE Trans. Robotics Autom..

[34]  Christian Antfolk,et al.  Decoding of individual finger movements from surface EMG signals using vector autoregressive hierarchical hidden Markov models (VARHHMM) , 2017, 2017 International Conference on Rehabilitation Robotics (ICORR).