Intelligent Robotics and Applications

Ankle rehabilitation exercise therapy mainly includes passive, active, and resistance rehabilitations. Active rehabilitation exercise is highly important during ankle rehabilitation, how to combine the ankle and the rehabilitation robot has elicited considerable research attention. This paper proposed a combination strategy for the ankle and the rehabilitation robot based on a 3D modeling of the ankle and the robot developed individually based on simulation software (AnyBody). By integrating the 3D models of the human body and the robot, setting constraints between the pedal of the robot and the pelma of the human body and the degrees of freedom of the ankle is constrained as 3, a man-machine integration model for ankle active rehabilitation strategy analysis was established. Then, human muscle characteristics were established under the combination of different variables and the range of ankle motion were carried out by the human body active movement with ankle plantar/dorsal flexion motion, this further leads to strategies for ankle active rehabilitation. Finally, this paper designs the driving function for the robot based on Fourier function, and the force condition for the ankle movement during rehabilitation exercise was evaluated from the perspec‐ tive of biomechanics. This study would provide a fundamental reference for the further formulation of active rehabilitation strategies and the control of rehabili‐ tation robots.

[1]  M. Bergamasco,et al.  A New Gaze-BCI-Driven Control of an Upper Limb Exoskeleton for Rehabilitation in Real-World Tasks , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[2]  N. Hogan,et al.  A working model of stroke recovery from rehabilitation robotics practitioners , 2009, Journal of NeuroEngineering and Rehabilitation.

[3]  Zhiwei Zhu,et al.  Eye and gaze tracking for interactive graphic display , 2002, SMARTGRAPH '02.

[4]  Masahiro Fujita,et al.  An ethological and emotional basis for human-robot interaction , 2003, Robotics Auton. Syst..

[5]  Erhan Akdoğan,et al.  The design and control of a therapeutic exercise robot for lower limb rehabilitation: Physiotherabot , 2011 .

[6]  M. Butz,et al.  Changes of cortico-muscular coherence: an early marker of healthy aging? , 2011, AGE.

[7]  Mark Ferraro,et al.  Continuous passive motion improves shoulder joint integrity following stroke , 2005, Clinical rehabilitation.

[8]  Peter W. McOwan,et al.  A real-time automated system for the recognition of human facial expressions , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[9]  Cynthia Breazeal,et al.  Recognition of Affective Communicative Intent in Robot-Directed Speech , 2002, Auton. Robots.

[10]  Vlodek Siemionow,et al.  Weakening of Corticomuscular Signal Coupling During Voluntary Motor Action in Aging. , 2015, The journals of gerontology. Series A, Biological sciences and medical sciences.

[11]  F C T van der Helm,et al.  Requirements for upper extremity motions during activities of daily living. , 2005, Clinical biomechanics.

[12]  A. Mihailidis,et al.  The development of an adaptive upper-limb stroke rehabilitation robotic system , 2011, Journal of NeuroEngineering and Rehabilitation.

[13]  Meta-analysis of transcranial magnetic stimulation to treat post-stroke dysfunction , 2011 .

[14]  Frédéric Lerasle,et al.  Two-handed gesture recognition and fusion with speech to command a robot , 2012, Auton. Robots.

[15]  Joachim Lange,et al.  Beta oscillations and their functional role in movement perception , 2014 .

[16]  Anatol G. Feldman,et al.  Interjoint coordination in lower limbs during different movements in humans , 2002, Experimental Brain Research.

[17]  Songpo Li,et al.  3-D-Gaze-Based Robotic Grasping Through Mimicking Human Visuomotor Function for People With Motion Impairments , 2017, IEEE Transactions on Biomedical Engineering.

[18]  Yang Guang-ying Surface Electromyography Analytical Method Based on the Parameter of AR Model , 2003 .

[19]  L. Ma Relationship between Handgrip Forces and Surface Electromyogram Activities of Forearm Muscle , 2007 .

[20]  Simon F Giszter,et al.  Motor primitives—new data and future questions , 2015, Current Opinion in Neurobiology.

[21]  Nikolaos G. Tsagarakis,et al.  On the Kinematic Motion Primitives (kMPs) – Theory and Application , 2012, Front. Neurorobot..

[22]  Warren Cornwall,et al.  In pursuit of the perfect power suit. , 2015, Science.

[23]  Ronglei Sun,et al.  Kinematic analysis and dexterity evaluation of upper extremity in activities of daily living. , 2010, Gait & posture.

[24]  N. Hogan,et al.  Motions or muscles? Some behavioral factors underlying robotic assistance of motor recovery. , 2006, Journal of rehabilitation research and development.

[25]  M. Betke,et al.  The Camera Mouse: visual tracking of body features to provide computer access for people with severe disabilities , 2002, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[26]  Luo Zhizeng Study of Myoelectric Bionic Artificial Hand with Tactile Sense , 2005 .

[27]  G. Koshland,et al.  Control of the wrist in three-joint arm movements to multiple directions in the horizontal plane. , 2000, Journal of neurophysiology.

[28]  Jaap Harlaar,et al.  Complete 3D kinematics of upper extremity functional tasks. , 2008, Gait & posture.

[29]  Liu Guang-yuan Emotion recognition based on wavelet packet entropy of surface EMG signal , 2008 .

[30]  William W. Abbott,et al.  3D gaze cursor: Continuous calibration and end-point grasp control of robotic actuators , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[31]  Xian-pingI Meng,et al.  The effects of low frequency electrical stimulation on connectivity changes in the brain and motor function after ischemic stroke , 2012 .

[32]  Tanu Sharma,et al.  A novel feature extraction for robust EMG pattern recognition , 2016, Journal of medical engineering & technology.

[33]  Jin-Shin Lai,et al.  Assistive Control System for Upper Limb Rehabilitation Robot , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[34]  W. Wong,et al.  Cerebral Plasticity After Subcortical Stroke as Revealed by Cortico-Muscular Coherence , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[35]  Francesco Lacquaniti,et al.  Modular Control of Limb Movements during Human Locomotion , 2007, The Journal of Neuroscience.

[36]  N. P. Reddy,et al.  Toward direct biocontrol using surface EMG signals: control of finger and wrist joint models. , 2007, Medical engineering & physics.

[37]  Arno H. A. Stienen,et al.  Implementation of EMG- and Force-Based Control Interfaces in Active Elbow Supports for Men With Duchenne Muscular Dystrophy: A Feasibility Study , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[38]  Caihua Xiong,et al.  Design and Implementation of an Anthropomorphic Hand for Replicating Human Grasping Functions , 2016, IEEE Transactions on Robotics.

[39]  Yoshiaki Hayashi,et al.  An EMG-Based Control for an Upper-Limb Power-Assist Exoskeleton Robot , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[40]  N. Scafetta,et al.  Spectral coherence between climate oscillations and the M ≥ 7 earthquake historical worldwide record , 2015, Natural Hazards.

[41]  Caihua Xiong,et al.  Synergistic Characteristic of Human Hand during Grasping Tasks in Daily Life , 2014, ICIRA.

[42]  Tatsuo Narikiyo,et al.  Proof of Concept for Robot-Aided Upper Limb Rehabilitation Using Disturbance Observers , 2015, IEEE Transactions on Human-Machine Systems.