Development of a Hand Exoskeleton System for Quantitative Analysis of Hand Functions

This paper proposes a hand exoskeleton system for evaluating hand functions. To evaluate hand functions, the hand exoskeleton system must be able to pull each finger joint, measure the finger joint angle and exerted force on the finger simultaneously. The proposed device uses serially connected 4-bar linkage structures, which have two embedded actuators with encoders and two loadcells per finger, to move each phalanx independently and measure the finger joint angles. A modular design was used for the exoskeleton, to facilitate the removal of unnecessary modules in different experiments and improve convenience. Silicon was used on the surface of the worn part to reduce the skin irritation that results from prolonged usage. This part was also designed to be compatible with various finger thicknesses. Using the proposed hand exoskeleton system, finger independence, multi-finger synergy, and finger joint stiffness were determined in five healthy subjects. The finger movement and force data collected in the experiments were used for analyzing three hand functions based on the physical and physiological phenomena.

[1]  K. Y. Tong,et al.  An EMG-driven exoskeleton hand robotic training device on chronic stroke subjects: Task training system for stroke rehabilitation , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

[2]  Zong-Ming Li,et al.  A robot‐assisted study of intrinsic muscle regulation on proximal interphalangeal joint stiffness by varying metacarpophalangeal joint position , 2006, Journal of orthopaedic research : official publication of the Orthopaedic Research Society.

[3]  Suin Kim,et al.  Force-Mode Control of Rotary Series Elastic Actuators in a Lower Extremity Exoskeleton Using Model-Inverse Time Delay Control , 2017, IEEE/ASME Transactions on Mechatronics.

[4]  D. Mozaffarian,et al.  Heart disease and stroke statistics--2014 update: a report from the American Heart Association. , 2014, Circulation.

[5]  Halla B. Olafsdottir,et al.  Is the thumb a fifth finger? A study of digit interaction during force production tasks , 2004, Experimental Brain Research.

[6]  R. J. Johns,et al.  Quantitative and Qualitative Analysis of Joint Stiffness in Normal Subjects and in Patients with Connective Tissue Diseases *† , 1961, Annals of the rheumatic diseases.

[7]  Serafeim Perdikis,et al.  mano: A Wearable Hand Exoskeleton for Activities of Daily Living and Neurorehabilitation , 2018, IEEE Robotics and Automation Letters.

[8]  M. Latash,et al.  Synergies in health and disease: relations to adaptive changes in motor coordination. , 2006, Physical therapy.

[9]  Lapo Governi,et al.  Kinematic synthesis and testing of a new portable hand exoskeleton , 2017 .

[10]  Julius Verrel Distributional properties and variance-stabilizing transformations for measures of uncontrolled manifold effects , 2010, Journal of Neuroscience Methods.

[11]  H Rodgers,et al.  A review of the properties and limitations of the Ashworth and modified Ashworth Scales as measures of spasticity , 1999, Clinical rehabilitation.

[12]  R. Meals,et al.  Proximal interphalangeal joint stiffness: measurement and analysis. , 2005, The Journal of hand surgery.

[13]  J M Mansour,et al.  An experimentally based nonlinear viscoelastic model of joint passive moment. , 1996, Journal of biomechanics.

[14]  N J Giori,et al.  Continuous passive motion (CPM): theory and principles of clinical application. , 2000, Journal of rehabilitation research and development.

[15]  Mario Cortese,et al.  A Powered Finger–Thumb Wearable Hand Exoskeleton With Self-Aligning Joint Axes , 2015, IEEE/ASME Transactions on Mechatronics.

[16]  Preeti Raghavan,et al.  Patterns of impairment in digit independence after subcortical stroke. , 2006, Journal of neurophysiology.

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

[18]  M. Latash,et al.  Learning multi-finger synergies: an uncontrolled manifold analysis , 2004, Experimental Brain Research.

[19]  M. Latash,et al.  Uncontrolled manifold analysis of single trials during multi-finger force production by persons with and without Down syndrome , 2003, Experimental Brain Research.

[20]  Michael Girard,et al.  Computer animation of knowledge-based human grasping , 1991, SIGGRAPH.

[21]  J. Littler The finger extensor mechanism. , 1967, The Surgical clinics of North America.

[22]  Elizabeth Rendon-Velez,et al.  Mechanisms for linkage-driven underactuated hand exoskeletons: conceptual design including anatomical and mechanical specifications , 2016 .

[23]  Jeongsoo Lee,et al.  Analysis of Finger Muscular Forces using a Wearable Hand Exoskeleton System , 2017 .

[24]  Wei Tech Ang,et al.  Analysis of Accuracy in Pointing with Redundant Hand-held Tools: A Geometric Approach to the Uncontrolled Manifold Method , 2013, PLoS Comput. Biol..

[25]  Marco Troncossi,et al.  An Original Classification of Rehabilitation Hand Exoskeletons , 2016 .

[26]  Jung Kim,et al.  Development of an MR-compatible hand exoskeleton that is capable of providing interactive robotic rehabilitation during fMRI imaging , 2018, Medical & Biological Engineering & Computing.

[27]  M. Schieber,et al.  Reduced muscle selectivity during individuated finger movements in humans after damage to the motor cortex or corticospinal tract. , 2004, Journal of neurophysiology.

[28]  M. Latash,et al.  Enslaving effects in multi-finger force production , 2000, Experimental Brain Research.

[29]  Rainer Koch,et al.  Reliability of the Modified Tardieu Scale and the Modified Ashworth Scale in adult patients with severe brain injury: a comparison study , 2005, Clinical rehabilitation.

[30]  Jae Kun Shim,et al.  Strength training increases training-specific multifinger coordination in humans. , 2008, Motor control.