scholarly journals Grasping Pattern Recognition and Grasping Force Estimation For Prosthetic Hands

2016 ◽  
Vol 7 ◽  
pp. 09016 ◽  
Author(s):  
Bing-Ke Zhang ◽  
Guo-Liang Zhong ◽  
Hua Deng
Keyword(s):  
Pattern Recognition ◽  
Force Estimation ◽  
Prosthetic Hands ◽  
Grasping Force

Hybrid Slip Detection Scheme Based Reflex Control of a Prosthetic Hand

2013 ◽  
Vol 433-435 ◽  
pp. 85-92
Author(s):  
Xue Feng Li ◽  
Xiao Gang Duan ◽  
Hua Deng
Keyword(s):  
Fuzzy Controller ◽  
Control Process ◽  
Prosthetic Hand ◽  
Force Estimation ◽  
Estimation Model ◽  
Detection Scheme ◽  
Prosthetic Hands ◽  
Accurate Judgment ◽  
Grasping Force ◽  
Reflex Control

Through the judgment of slip or not, human make proper adjustment to the grasping force and achieve stable manipulations. To reconstruct this function on a prosthetic hands platform, this paper presents a hybrid slip detect algorithm utilizing the PVDF sensor and FSR sensor. Then a reflex force estimation model is built to quantify the reflex force according to the intensity of slip in the reflex control process. Finally, through comparative experiments, the anti-jamming performance of the hybrid slip detect scheme is tested. A fuzzy controller is used to control the applied force and test the whole reflex control system. The results show that the hybrid slip detect scheme can make accurate judgment and has strong anti-jamming capacity; The output of the reflex force estimation model is accordance with the factual case; And as a whole, the grasping ability of prosthetic hand is substantially enhanced.

scholarly journals A Finger Grip Force Sensor with an Open-Pad Structure for Glove-Type Assistive Devices

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 4 ◽  
Author(s):  
Junghoon Park ◽  
Pilwon Heo ◽  
Jung Kim ◽  
Youngjin Na
Keyword(s):  
Degrees Of Freedom ◽  
Grip Force ◽  
Force Sensor ◽  
Assistive Devices ◽  
Finger Joint ◽  
Assistive Device ◽  
Force Estimation ◽  
Joint Angles ◽  
Fingertip Force ◽  
Grasping Force

This paper presents a fingertip grip force sensor based on custom capacitive sensors for glove-type assistive devices with an open-pad structure. The design of the sensor allows using human tactile sensations during grasping and manipulating an object. The proposed sensor can be attached on both sides of the fingertip and measure the force caused by the expansion of the fingertip tissue when a grasping force is applied to the fingertip. The number of measurable degrees of freedom (DoFs) are the two DoFs (flexion and adduction) for the thumb and one DoF (flexion) for the index and middle fingers. The proposed sensor allows the combination with a glove-type assistive device to measure the fingertip force. Calibration was performed for each finger joint angle because the variations in the expansion of the fingertip tissue depend on the joint angles. The root mean square error (RMSE) for fingertip force estimation ranged from 3.75% to 9.71% after calibration, regardless of the finger joint angles or finger posture.

Grasping Force Estimation by sEMG Signals and Arm Posture: Tensor Decomposition Approach

2019 ◽  
Vol 16 (3) ◽  
pp. 455-467 ◽  
Author(s):  
Sanghyun Kim ◽  
Joowan Kim ◽  
Mingon Kim ◽  
Seungyeon Kim ◽  
Jaeheung Park
Keyword(s):  
Tensor Decomposition ◽  
Force Estimation ◽  
Decomposition Approach ◽  
Grasping Force ◽  
Semg Signals

Grasping force estimation in robotic forceps using a soft pneumatic actuator with a built-in sensor

2018 ◽  
Vol 271 ◽  
pp. 124-130 ◽  
Author(s):  
Takashi Takizawa ◽  
Takahiro Kanno ◽  
Ryoken Miyazaki ◽  
Kotaro Tadano ◽  
Kenji Kawashima
Keyword(s):  
Pneumatic Actuator ◽  
Force Estimation ◽  
Grasping Force

scholarly journals Control of prosthetic hand by using mechanomyography signals based on support-vector machine classifier

2021 ◽  
Vol 23 (2) ◽  
pp. 1180
Author(s):  
Firas Saaduldeen Ahmed ◽  
Noha Abed-Al-Bary Al-jawady
Keyword(s):  
Pattern Recognition ◽  
Support Vector Machine ◽  
Wavelet Packet ◽  
Support Vector ◽  
Prosthetic Hand ◽  
Hand Gestures ◽  
Prosthetic Devices ◽  
Prosthetic Hands ◽  
Average Accuracy ◽  
And Control

<div>Prosthetic devices are necessary to help amputees achieve their daily activity in the natural way possible. The prosthetic hand has controlled by type of signals such as electromyography (EMG) and mechanomyography (MMG). The MMG signals have represented mechanical signals that generate during muscle contraction. These signals can be detected by accelerometers or microphones and any kind of sensors that can detect muscle vibrations. The contribution of the current paper is classifying hand gestures and control prosthetic hands depends on pattern recognition through accelerometer and microphone are to detect MMG signals. In addition to the cost of prosthetic hand less than other designs. Six subjects are involved. In this present work is the devices. In this study, two of them are amputee subjects. Each subject performs seven classes of movements. Pattern recognition (PR) is used to classify hand gestures. The wavelet packet transform (WPT) and root mean square (RMS) as features extracted from the signals and support vector machine (SVM) as a classifier. The average accuracy is 88.94% for offline tests and 84.45% for online tests. 3D printing technology is used in this study to build prosthetic hands.</div>

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