A novel upper limb training system based on UR5 using sEMG and IMU sensors

Liu, Zhenqiang; Chang, Wennan; Sheng, Shili; Li, Liang; Soo, Yew Guan; Yeong, Che Fai; Odagaki, Masato; Duan, Feng

doi:10.1109/robio.2016.7866467

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…The EMG signals were recorded and classified using K-Nearest Neighbours (K-NN) and Linear Discriminant Analysis (LDA) where LDA has become the most accurate classifier method obtained [10][11][12]. A portable sEMG sensor however can measure the hand gesture through the acquisition of muscle electrical activity using less sensor requirement [2], [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Analysis of surface electromyography for hand gesure classification

Ilyana

Sharvin

et al. 2019

IJEECS

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<span lang="EN-MY">Electromyography (EMG) is the measure of electrical activity produced by skeletal muscle. It is useful in prosthetic and rehabilitation technology as well as ability to handle electronic devices and robotics. If the EMG signal from the body especially hand movement can be apprehended, better value for people all around the world can be provided. Furthermore, it can be used to control smart-phone and be integrated with wearable technology. Another interesting application of this technology is in sign language recognition which is able to assist many disabled people in their daily lives. In this paper, hand gesture signals are acquired, extracted, analysed and classified. The EMG data from hand gesture which are rock, paper and scissors managed to be extracted. We use time domain feature to classified using Principal Component Analysis and regression tree. The result was highly accurate with 72.59% and 80.85% for PCA and regression tree respectively.</span>

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Section: Introductionmentioning

confidence: 99%

Analysis of surface electromyography for hand gesure classification

Ilyana

Sharvin

et al. 2019

IJEECS

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“…The robotic arm used in this paper was a UR5 six-axis robot (Universal Robot 5). 3,4 The six-axis control involves the kinamatics 5 of the Denavit–Hartenberg (D–H) transformation matrix, 6,7 dynamics, as well as control theory, 8,9 including the computation of six-axis rotational angles from the work point and pose of the robotic arm and the derivation of spatial work point position and pose from rotational angles to complete spatial track planning, 10 etc.…”

Section: Introductionmentioning

confidence: 99%

An image vision and automatic calibration system for universal robots

Jian

Tsai

Kuo

et al. 2019

Journal of Low Frequency Noise, Vibration and Active Control

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The rapid development of technology is causing the replacement of many traditional manufacture industries by automation. Robotic arms are now commonly used in many sectors. The requirements for robotic arms in different sectors are quite different; but, in general, robotic arms not only save cost and man power, they also improve safety. The aim of this study was an investigation of the integration of image identification with robotic arms. To do this, the Denavit–Hartenberg transformation matrix was used to analyze the mechanical kinematics of the joints of each robotic arm axis. This allowed the spatial relationships between the Cartesian 3 D coordinate system and the joint of each axis to be determined and communication between the robotic arm and image identification to be established. A robotic arm prototype platform with automatic image identification and calibration was constructed using a quick and robust method. Several of the variables that exist in real robotic arm applications have been solved in this study: primarily the accuracy error that occurs when repeated gripping of workpieces is done and a movement and placement track is set up. Deviations occur frequently and if image identification can be applied to offset repeated inaccuracy, the quality of finished products will be more consistent. The Universal Robots 5 six-axis robot and cameras, which use a filter, Sobel computation, and Hough transformation computer vision image processing technology, together comprise a system that can automatically identify the workpiece and carry out loading and unloading accurately.

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Calibration of Low Cost IMU’s Inertial Sensors for Improved Attitude Estimation

Dong

Yao

et al. 2020

J Intell Robot Syst

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A novel upper limb training system based on UR5 using sEMG and IMU sensors

Cited by 5 publications

References 15 publications

Analysis of surface electromyography for hand gesure classification

Analysis of surface electromyography for hand gesure classification

An image vision and automatic calibration system for universal robots

Calibration of Low Cost IMU’s Inertial Sensors for Improved Attitude Estimation

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