Prediction and Fitting of Nonlinear Dynamic Grip Force of the Human Upper Limb Based on Surface Electromyographic Signals.

Zixiang Cai, Mengyao Qu, Mingyang Han, Zhijing Wu, Tong Wu, Mengtong Liu, Hailong Yu

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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3 · Its place in the literature

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1 citing paper in PubMed.

Flexible Strain Sensor Based on PVA/Tannic Acid/Lithium Chloride Ionically Conductive Hydrogel with Excellent Sensing and Good Adhesive Properties.Sensors (Basel, Switzerland) · 2025
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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Zixiang CaiSchool of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Mengyao QuSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Mingyang HanSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Zhijing WuSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Tong WuSchool of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Mengtong LiuSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Hailong YuSchool of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to predict and fit the nonlinear dynamic grip force of the human upper limb using surface electromyographic (sEMG) signals. The research employed a time-series-based neural network, NARX, to establish a mapping relationship between the electromyographic signals of the forearm muscle groups and dynamic grip force. Three-channel electromyographic signal acquisition equipment and a grip force sensor were used to record muscle signals and grip force data of the subjects under specific dynamic force conditions. After preprocessing the data, including outlier removal, wavelet denoising, and baseline drift correction, the NARX model was used for fitting analysis. The model compares two different training strategies: regularized stochastic gradient descent (BRSGD) and conjugate gradient (CG). The results show that the CG greatly shortened the training time, and performance did not decline. NARX demonstrated good accuracy and stability in dynamic grip force prediction, with the model with 10 layers and 20 time delays performing the best. The results demonstrate that the proposed method has potential practical significance for force control applications in smart prosthetics and virtual reality.

Indexed as

ElectromyographyHand StrengthUpper ExtremityAdultAlgorithmsFemaleForearmHumansMaleMuscle, SkeletalNeural Networks, ComputerNonlinear DynamicsSignal Processing, Computer-AssistedYoung Adultgrip force predictionNARX neural networknonlinear dynamic grip forcesurface electromyographic signals

Identifiers

PMID39796806

DOI10.3390/s25010013

PMCPMC11722905

What Socratic holds

Textmetadata

LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.