ArticleSensors (Basel, Switzerland)2021
Action Recognition of Lower Limbs Based on Surface Electromyography Weighted Feature Method.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Mechanomyography Signal Pattern Recognition of Knee and Ankle Movements Using Swarm Intelligence Algorithm-Based Feature Selection Methods.Sensors (Basel, Switzerland) · 2023Article
- Surface electromyogram, kinematic, and kinetic dataset of lower limb walking for movement intent recognition.Scientific data · 2023Article
- Human Action Recognition: A Paradigm of Best Deep Learning Features Selection and Serial Based Extended Fusion.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
To improve the recognition rate of lower limb actions based on surface electromyography (sEMG), an effective weighted feature method is proposed, and an improved genetic algorithm support vector machine (IGA-SVM) is designed in this paper. First, for the problem of high feature redundancy and low discrimination in the surface electromyography feature extraction process, the weighted feature method is proposed based on the correlation between muscles and actions. Second, to solve the problem of the genetic algorithm selection operator easily falling into a local optimum solution, the improved genetic algorithm-support vector machine is designed by championship with sorting method. Finally, the proposed method is used to recognize six types of lower limb actions designed, and the average recognition rate reaches 94.75%. Experimental results indicate that the proposed method has definite potentiality in lower limb action recognition.
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Registered trials
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