sEMG Feature Optimization Strategy for Finger Grip Force Estimation

Windturbine blade fault diagnosis based on MFCC feature optimization strategy

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/675/1/012074 ◽

2021 ◽

Vol 675 (1) ◽

pp. 012074

Author(s):

Yuxing Wang ◽

Jianxin Tan ◽

Xiaoliang Qin ◽

Zhanfei Hu ◽

Yanwei Jing ◽

...

Keyword(s):

Fault Diagnosis ◽

Optimization Strategy ◽

Feature Optimization

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

Sensors ◽

10.3390/s20010004 ◽

2019 ◽

Vol 20 (1) ◽

pp. 4 ◽

Cited By ~ 1

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.

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Computational prediction and analysis of species-specific fungi phosphorylation via feature optimization strategy

Briefings in Bioinformatics ◽

10.1093/bib/bby122 ◽

2018 ◽

Vol 21 (2) ◽

pp. 595-608 ◽

Cited By ~ 4

Author(s):

Man Cao ◽

Guodong Chen ◽

Jialin Yu ◽

Shaoping Shi

Keyword(s):

Protein Phosphorylation ◽

Computational Prediction ◽

Optimization Method ◽

Phosphorylation Sites ◽

Optimization Strategy ◽

Post Translational Modification ◽

Tyrosine Residues ◽

Identification Of Fungi ◽

Feature Optimization ◽

Species Specific

Abstract Protein phosphorylation is a reversible and ubiquitous post-translational modification that primarily occurs at serine, threonine and tyrosine residues and regulates a variety of biological processes. In this paper, we first briefly summarized the current progresses in computational prediction of eukaryotic protein phosphorylation sites, which mainly focused on animals and plants, especially on human, with a less extent on fungi. Since the number of identified fungi phosphorylation sites has greatly increased in a wide variety of organisms and their roles in pathological physiology still remain largely unknown, more attention has been paid on the identification of fungi-specific phosphorylation. Here, experimental fungi phosphorylation sites data were collected and most of the sites were classified into different types to be encoded with various features and trained via a two-step feature optimization method. A novel method for prediction of species-specific fungi phosphorylation-PreSSFP was developed, which can identify fungi phosphorylation in seven species for specific serine, threonine and tyrosine residues (http://computbiol.ncu.edu.cn/PreSSFP). Meanwhile, we critically evaluated the performance of PreSSFP and compared it with other existing tools. The satisfying results showed that PreSSFP is a robust predictor. Feature analyses exhibited that there have some significant differences among seven species. The species-specific prediction via two-step feature optimization method to mine important features for training could considerably improve the prediction performance. We anticipate that our study provides a new lead for future computational analysis of fungi phosphorylation.

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