Hybrid Brain/Muscle Signals Powered Wearable Walking Exoskeleton Enhancing Motor Ability in Climbing Stairs Activity

The powered exoskeleton promises substantial improvements on daily activities of the people who need robots provide assistance. In order to achieve flexible and stable control of a powered lower limb exoskeleton, in this paper, a hybrid control that combines a brain-computer interface (BCI) based on motor imagery (MI) with surface electromyogram (EMG) signals has been developed. We utilized the common spatial pattern (CSP) method to extract the variance of electroencephalogram (EEG) signals and back propagation (BP) neural network to recognize the imagery tasks. Moreover, we have used the strength of EMG signals obtained from upper forearms of subjects to adjust the gait of exoskeleton robots according to real stairs so that subjects can climb stairs easily and stably. The recognized results of EEG and the strength of EMG are used to drive the powered exoskeleton to help subjects climb the stairs by the designed gait synthesis which satisfies the environmental constraint and kinematic constraint. The developed hybrid control strategy has been verified by three healthy subjects, and all subjects can successfully fulfill steadily climbing the stairs, assisted by the powered exoskeleton. The results of the experiment have demonstrated the developed hybrid brain/muscle signals powered robot can effectively enhance human mobility.

[1]  Michael Goldfarb,et al.  A Preliminary Assessment of Legged Mobility Provided by a Lower Limb Exoskeleton for Persons With Paraplegia , 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[2]  Febo Cincotti,et al.  Tools for Brain-Computer Interaction: A General Concept for a Hybrid BCI , 2011, Front. Neuroinform..

[3]  Rong Song,et al.  Movement Performance of Human–Robot Cooperation Control Based on EMG-Driven Hill-Type and Proportional Models for an Ankle Power-Assist Exoskeleton Robot , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[4]  Jon A. Mukand,et al.  Neuronal ensemble control of prosthetic devices by a human with tetraplegia , 2006, Nature.

[5]  Youngjin Choi,et al.  EMG-Based Continuous Control Scheme With Simple Classifier for Electric-Powered Wheelchair , 2016, IEEE Transactions on Industrial Electronics.

[6]  Yoshiaki Hayashi,et al.  Stairs-ascending/descending assist for a lower-limb power-assist robot considering ZMP , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[7]  Hiroshi Yokoi,et al.  Hybrid EEG-EOG system for intelligent prosthesis control based on common spatial pattern algorithm , 2016, 2016 IEEE International Conference on Information and Automation (ICIA).

[8]  Michael J. Black,et al.  Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array , 2011 .

[9]  J. Donoghue,et al.  Primary Motor Cortex Tuning to Intended Movement Kinematics in Humans with Tetraplegia , 2008, The Journal of Neuroscience.

[10]  Yuehong Yin,et al.  Active and Progressive Exoskeleton Rehabilitation Using Multisource Information Fusion From EMG and Force-Position EPP , 2013, IEEE Transactions on Biomedical Engineering.

[11]  Nicolas Y. Masse,et al.  Reach and grasp by people with tetraplegia using a neurally controlled robotic arm , 2012, Nature.

[12]  XiaoQi Chen,et al.  Preliminary Evaluation of Intelligent Intention Estimation Algorithms for an Actuated Lower-Limb Exoskeleton , 2013 .

[13]  Gert Pfurtscheller,et al.  Motor imagery and direct brain-computer communication , 2001, Proc. IEEE.

[14]  Thilo Hinterberger,et al.  An Auditory Brain-Computer Interface Based on the Self-Regulation of Slow Cortical Potentials , 2005, Neurorehabilitation and neural repair.

[15]  S Micera,et al.  Control of Hand Prostheses Using Peripheral Information , 2010, IEEE Reviews in Biomedical Engineering.

[16]  J. Wolpaw,et al.  Brain-Computer Interfaces: Principles and Practice , 2012 .

[17]  Xinjun Sheng,et al.  Toward an Enhanced Human–Machine Interface for Upper-Limb Prosthesis Control With Combined EMG and NIRS Signals , 2017, IEEE Transactions on Human-Machine Systems.

[18]  Denis Delisle-Rodriguez,et al.  Control of a robotic knee exoskeleton for assistance and rehabilitation based on motion intention from sEMG , 2018, Research on Biomedical Engineering.

[19]  Andrés Úbeda,et al.  Visual evoked potential-based brain-machine interface applications to assist disabled people , 2012, Expert Syst. Appl..

[20]  Levi J. Hargrove,et al.  Detection of and Compensation for EMG Disturbances for Powered Lower Limb Prosthesis Control , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[21]  R. Andersen,et al.  Decoding motor imagery from the posterior parietal cortex of a tetraplegic human , 2015, Science.

[22]  Iñaki Iturrate,et al.  A Noninvasive Brain-Actuated Wheelchair Based on a P300 Neurophysiological Protocol and Automated Navigation , 2009, IEEE Transactions on Robotics.

[23]  J. Ushiba,et al.  Effects of neurofeedback training with an electroencephalogram-based brain-computer interface for hand paralysis in patients with chronic stroke: a preliminary case series study. , 2011, Journal of rehabilitation medicine.

[24]  Muhammad Abd-El-Barr,et al.  Long-term Training With a Brain-Machine Interface-Based Gait Protocol Induces Partial Neurological Recovery in Paraplegic Patients. , 2016, Neurosurgery.

[25]  T. Pedley,et al.  Beta and Mu Rhythms , 1990, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[26]  Xiaorong Gao,et al.  A BCI-based environmental controller for the motion-disabled , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[27]  Han-Pang Huang,et al.  Bayesian human intention estimator for exoskeleton system , 2013, 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

[28]  Shangkai Gao,et al.  A practical VEP-based brain-computer interface. , 2006, IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[29]  Michael J. Black,et al.  Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia , 2008, Journal of neural engineering.

[30]  F. L. D. Silva,et al.  Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.

[31]  Ahmed Aia,et al.  Variable Admittance Control for Climbing Stairs in Human-Powered Exoskeleton Systems , 2016, ICRA 2016.

[32]  Tom Carlson,et al.  Muscular activity and physical interaction forces during lower limb exoskeleton use. , 2016, Healthcare technology letters.

[33]  Byeonghun Na,et al.  WalkON Suit: A Medalist in the Powered Exoskeleton Race of Cybathlon 2016 , 2017, IEEE Robotics & Automation Magazine.

[34]  Michael Goldfarb,et al.  An Assistive Control Approach for a Lower-Limb Exoskeleton to Facilitate Recovery of Walking Following Stroke , 2015, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[35]  Chun-Yi Su,et al.  Brain–Machine Interface and Visual Compressive Sensing-Based Teleoperation Control of an Exoskeleton Robot , 2017, IEEE Transactions on Fuzzy Systems.

[36]  Thorsten O. Zander,et al.  Combining Eye Gaze Input With a Brain–Computer Interface for Touchless Human–Computer Interaction , 2010, Int. J. Hum. Comput. Interact..

[37]  T. Martin McGinnity,et al.  EEG-Based Mobile Robot Control Through an Adaptive Brain–Robot Interface , 2014, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[38]  Takeshi Ando,et al.  Analysis of EMG signals of patients with essential tremor focusing on the change of tremor frequency , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[39]  Xingyu Wang,et al.  Author's Personal Copy Biomedical Signal Processing and Control Lasso Based Stimulus Frequency Recognition Model for Ssvep Bcis , 2022 .

[40]  Karim Djouani,et al.  Toward Lower Limbs Functional Rehabilitation Through a Knee-Joint Exoskeleton , 2017, IEEE Transactions on Control Systems Technology.

[41]  Yue Zhao,et al.  A Wireless BCI and BMI System for Wearable Robots , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[42]  Nicola Vitiello,et al.  An EEG/EOG-based hybrid brain-neural computer interaction (BNCI) system to control an exoskeleton for the paralyzed hand , 2015, Biomedizinische Technik. Biomedical engineering.

[43]  Dong Liu,et al.  A brain-controlled exoskeleton with cascaded event-related desynchronization classifiers , 2017, Robotics Auton. Syst..

[44]  Yasuhisa Hasegawa,et al.  Restoration of Gait for Spinal Cord Injury Patients Using HAL With Intention Estimator for Preferable Swing Speed , 2015, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[45]  Zhijun Li,et al.  Human-Cooperative Control of a Wearable Walking Exoskeleton for Enhancing Climbing Stair Activities , 2020, IEEE Transactions on Industrial Electronics.

[46]  Ying Feng,et al.  Human-Cooperative Control Design of a Walking Exoskeleton for Body Weight Support , 2020, IEEE Transactions on Industrial Informatics.