A novel multi-objective shielding optimization method: DNN-PCA-NSGA-Ⅱ

Song, Yimeng; Mao, Jie; Zhang, Zehuan; Huang, Hudie; Yuan, Weiwei; Chen, Yang

doi:10.1016/j.anucene.2021.108461

Cited by 15 publications

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is now established that high offline precision, does not necessarily translate into accurate functional control of a physical prosthesis. In this sense, several recent studies have shown the discrepancy between "on and offline" in performance metrics [7][8][9][10][11]. However, very few studies have published validation results of pattern recognition in terms, and even fewer in clinical settings, relating the variability of the signal and the performance of the classifiers with those parameters related to amputation (disability index, length remaining limb, amputation time, phantom limb sensation, etc.)…”

Section: Introductionmentioning

confidence: 99%

Perspective Chapter: Classification of Grasping Gestures for Robotic Hand Prostheses Using Deep Neural Networks

Serrezuela¹,

Reyes²,

Zamora³

et al. 2023

Human-Robot Interaction - Perspectives and Applications

View full text Add to dashboard Cite

This research compares classification accuracy obtained with the classical classification techniques and the presented convolutional neural network for the recognition of hand gestures used in robotic prostheses for transradial amputees using surface electromyography (sEMG) signals. The first two classifiers are the most used in the literature: support vector machines (SVM) and artificial neural networks (ANN). A new convolutional neural network (CNN) architecture based on the AtzoriNet network is proposed to assess performance according to amputation-related variables. The results show that convolutional neural networks with a very simple architecture can produce accurate results comparable to the average classical classification methods and The performance it is compared with other CNN proposed by other authors. The performance of the CNN is evaluated with different metrics, providing good results compared to those proposed by other authors in the literature.

show abstract