Design of a Regression Model for Four Grasping Patterns and Three Grip Force Intensities of a Myoelectric Prosthetic Hand

Noh, Jiho; Cho, Woorim; Kim, Jaehyo

doi:10.7736/kspe.2018.35.8.809

Cited by 3 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The new model ( GAPSO-SVM ) had higher prediction accuracy and reliability, with an average accuracy of up to 98.7 %. According to Jiho Noh and others, wavelet packet was used to decompose s EMG signal, and extracted the energy and variance of wavelet packet coefficients of s EMG signal as feature vectors [5]. SVM classifier was used to effectively identify six commonly used upper limb movements, and the average recognition rate was 90.66 %.…”

Section: Introductionmentioning

confidence: 99%

Four-channel sEMG for Elbow Load Recognition

Cai

Song

Long

2021

Preprint

View full text Add to dashboard Cite

In order to help patients after surgery to carry out reasonable rehabilitation training, avoid joint adhesions and movement disorders, the relationship between surface electromyograph (sEMG) signal changes and the size of the patient ' s joint force in the process of rehabilitation exercise was studied, hoping to use the relationship between them to redesign the control mode of the rehabilitation robot, and a method was proposed to identify the size of the elbow load based on wavelet packet. Firstly, s EMG signals of human elbow joint during stretching and bending under different loads were collected by 4-channel surface electromyography. Then, the wavelet packet decomposition method was used to obtain the feature vector composed of energy(E), variance(VAR) and mean absolute value(MAV) of wavelet packet coefficient. Finally, the improved support vector machine ( ISVM), BP neural network and RBF neural network were used for pattern recognition of three different forces. The experimental results show that the change of sEMG signal is indeed related to the size of joint force. It is feasible to identify the load of s EMG signal.

show abstract

Section: Introductionmentioning

confidence: 99%

Four-channel sEMG for Elbow Load Recognition

Cai

Song

Long

2021

Preprint

View full text Add to dashboard Cite

show abstract

Continuous grip force estimation from surface electromyography using generalized regression neural network

Mao

Peng

Zheng

et al. 2023

THC

View full text Add to dashboard Cite

BACKGROUND: Grip force estimation is highly required in realizing flexible and accurate prosthetic control. OBJECTIVE: This study presents a method to accurately estimate continuous grip force from surface electromyography (sEMG) under three forearm postures for unilateral amputees. METHODS: Ten able-bodied subjects and a transradial amputee were recruited. sEMG signals were recorded from six forearm muscles on the dominant side of each able-bodied subject and the stump of amputee. Meanwhile, grip force was synchronously measured from the ipsilateral hands of able-bodied subjects and contralateral hand of amputee. Three force profiles (triangle, trapezoid, and fast triangle) were tested under three forearm postures (supination, neutral and pronation). Two algorithms (Generalized Regression Neural Network (GRNN) and Multilinear Regression Model (MLR)) were compared using several EMG features. The estimation performance was evaluated by coefficient of determination (R2) and mean absolute error (MAE). RESULTS: The optimal regressor combining TD and GRNN achieved R2= 96.33 ± 1.13% and MAE= 2.11 ± 0.52% for the intact subjects, and R2= 86.86% and MAE= 2.13% for the amputee. The results indicated that multiple grip force curves under three forearm postures could be accurately estimated for unilateral amputees using mirrored bilateral training. CONCLUSIONS: The proposed method has the potential for precise force control of prosthetic hands.

show abstract

Design of Prosthetic Robot Hand and Electromyography-Based Hand Motion Recognition

Jang¹,

Sohn²

2020

KSPE

View full text Add to dashboard Cite

Design of a Regression Model for Four Grasping Patterns and Three Grip Force Intensities of a Myoelectric Prosthetic Hand

Cited by 3 publications

References 12 publications

Four-channel sEMG for Elbow Load Recognition

Four-channel sEMG for Elbow Load Recognition

Continuous grip force estimation from surface electromyography using generalized regression neural network

Design of Prosthetic Robot Hand and Electromyography-Based Hand Motion Recognition

Contact Info

Product

Resources

About