Design and Integration of a Two-Digit Exoskeleton Glove

2017 ◽  
Author(s):  
Eric Refour ◽  
Bijo Sebastain ◽  
Pinhas Ben-Tzvi
Keyword(s):  
Experimental Validation ◽  
Index Finger ◽  
Human Hand ◽  
Interaction Forces ◽  
Single Degree Of Freedom ◽  
Single Degree ◽  
Coupling Relationship ◽  
Active Flexion ◽  
Normal Human ◽  
Flexion And Extension

This paper presents the design and integration of a two-digit exoskeleton glove. The proposed glove is designed to assist the user with grasping motions, such as the pincer grasp, while maintaining a natural coupling relationship among the finger and thumb joints, resembling that of a normal human hand. The design employs single degree of freedom linkage mechanisms to achieve active flexion and extension of the index finger and thumb. This greatly reduces the overall weight and size of the system making it ideal for prolonged usage. The paper describes the design, mathematical modeling of the proposed system, detailed electromechanical design, and software architecture of the integrated prototype. The prototype is capable of recording information about the index finger and thumb movements, interaction forces exerted by the finger/thumb on the exoskeleton, and can provide feedback through vibration. In addition, the glove can serve as a standalone device for rehabilitation purposes, such as assisting in achieving tip or pulp pinch. The paper concludes with an experimental validation of the proposed design by comparing the motion produced using the exoskeleton glove on a wooden mannequin with that of a natural human hand.

Two-Digit Robotic Exoskeleton Glove Mechanism: Design and Integration

2018 ◽  
Vol 10 (2) ◽  
Author(s):  
Eric Refour ◽  
Bijo Sebastian ◽  
Pinhas Ben-Tzvi
Keyword(s):  
Experimental Validation ◽  
Index Finger ◽  
Human Hand ◽  
Interaction Forces ◽  
Single Degree Of Freedom ◽  
Robotic Exoskeleton ◽  
Coupling Relationship ◽  
Active Flexion ◽  
Normal Human ◽  
Flexion And Extension

This paper presents the design and integration of a two-digit robotic exoskeleton glove mechanism. The proposed glove is designed to assist the user with grasping motions, such as the pincer grasp, while maintaining a natural coupling relationship among the finger and thumb joints, resembling that of a normal human hand. The design employs single degree-of-freedom (DOF) linkage mechanisms to achieve active flexion and extension of the index finger and thumb. This greatly reduces the overall weight and size of the system making it ideal for prolonged usage. The paper describes the design, mathematical modeling of the proposed system, detailed electromechanical design, and software architecture of the integrated prototype. The prototype is capable of recording information about the index finger and thumb movements, interaction forces exerted by the finger/thumb on the exoskeleton, and can provide feedback through vibration. In addition, the glove can serve as a standalone device for rehabilitation purposes, such as assisting in achieving tip or pulp pinch. The paper concludes with an experimental validation of the proposed design by comparing the motion produced using the exoskeleton glove on a wooden mannequin with that of a natural human hand.

scholarly journals Identification of 7 Movements of the Human Hand Using sEMG - 360° on the Forearm

2021 ◽  
Author(s):  
◽  
A. Ibarra-Fuentes
Keyword(s):  
Time Domain ◽  
Index Finger ◽  
Ring Finger ◽  
Middle Finger ◽  
Svm Classifier ◽  
Human Hand ◽  
Absolute Value ◽  
The Time Domain ◽  
Flexion And Extension ◽  
Semg Signals

This document shows the identification of 7 gestures (movements) of the human hand from sEMG – 360° signals in the forearm. sEMG – 360° is the sEMG measurement through 8 channels every 45° making a total of 360°. When making a hand gesture, there will be 8 independents sEMG signals that will be used to identify the movement. The 7 gestures to identify are: relaxed hand (closed), open hand (fingers extended), flexion and extension of the little finger, the ring finger, the middle finger, the index finger and the thumb separately. 100 samples of each of the gesture were captured and 3 feature extractors were applied in the time domain (mean absolute value (MAV), root mean square value (RMS) and area vale under the curve (CUA)), then a vector support machine (SVM) classifier was applied to each extractor. The movements were identified and the percentage of accuracy in the identification was calculated for each extractor + SVM classifier. The calculation of the percentage of accuracy took into account the 8 channels for each gesture. 97.61 % accuracy was achieved in the identification of human hand gestures by applying sEMG – 360°.

A Single-DOF Multi-Function Prosthetic Hand Mechanism With an Automatically Variable Speed Transmission

1992 ◽  
Author(s):  
Gongliang Guo ◽  
Xikang Qian ◽  
Willim A. Gruver
Keyword(s):  
Human Hand ◽  
Prosthetic Hand ◽  
Variable Speed ◽  
Prosthetic Device ◽  
Single Degree Of Freedom ◽  
Single Degree ◽  
Flexion Extension ◽  
Robotic Applications ◽  
Fingertip Forces ◽  
Human Hands

Abstract This research concerns the design of a new three-fingered anthropomorphic hand mechanism for prosthetic and robotic applications. Based on the configuration and flexion/extension features of human hands, we develop a three-jointed finger mechanism using a gear-constrained planar five-bar linkage with a single degree of freedom (DOF). From an operational study of human hands, we also propose a multi-functional palm mechanism using a cam-groove submechanism. The hand can perform grasping, holding and pinching operations. To achieve the automatic shape adaptability of a human hand, a selfadaptable submechanism was designed within the palm based on the lever principle. An automatically variable speed transmission with selfadaptability was developed for this hand to achieve optimal flexing speed and optimal fingertip forces. This paper describes the mechanical structure and operational principles of this new prosthetic device.

scholarly journals A cross-species neural integration of gravity for motor optimization

2021 ◽  
Vol 7 (15) ◽  
pp. eabf7800
Author(s):  
Jeremie Gaveau ◽  
Sidney Grospretre ◽  
Bastien Berret ◽  
Dora E. Angelaki ◽  
Charalambos Papaxanthis
Keyword(s):  
Optimization Strategy ◽  
Motor Patterns ◽  
Activation Patterns ◽  
Single Degree Of Freedom ◽  
Single Degree ◽  
Neural Integration ◽  
Gravity Effects ◽  
Neural Signature ◽  
Experimental Findings ◽  
Muscular Activation

Recent kinematic results, combined with model simulations, have provided support for the hypothesis that the human brain shapes motor patterns that use gravity effects to minimize muscle effort. Because many different muscular activation patterns can give rise to the same trajectory, here, we specifically investigate gravity-related movement properties by analyzing muscular activation patterns during single-degree-of-freedom arm movements in various directions. Using a well-known decomposition method of tonic and phasic electromyographic activities, we demonstrate that phasic electromyograms (EMGs) present systematic negative phases. This negativity reveals the optimal motor plan’s neural signature, where the motor system harvests the mechanical effects of gravity to accelerate downward and decelerate upward movements, thereby saving muscle effort. We compare experimental findings in humans to monkeys, generalizing the Effort-optimization strategy across species.

Kinematic comparison of single degree-of-freedom robotic gait trainers

2021 ◽  
Vol 159 ◽  
pp. 104258
Author(s):  
Jeonghwan Lee ◽  
Lailu Li ◽  
Sung Yul Shin ◽  
Ashish D. Deshpande ◽  
James Sulzer
Keyword(s):  
Degree Of Freedom ◽  
Single Degree Of Freedom ◽  
Single Degree ◽  
Robotic Gait
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