ArticlePloS one2021
A deep learning-based method for grip strength prediction: Comparison of multilayer perceptron and polynomial regression approaches.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Grip Strength Estimation Using Input Data From a Commodity Smartphone: Model Development and Validation Study.JMIR human factors · 2026Article
- Machine Learning-Based Model for Grip Strength Prediction in Healthy Adults: A Nationwide Dataset-Based Study.Journal of clinical medicine · 2025Article
- Introduction of AI Technology for Objective Physical Function Assessment.Bioengineering (Basel, Switzerland) · 2024Review
- Grip and pinch strength prediction models based on hand anthropometric parameters: an analytic cross-sectional study.BMC musculoskeletal disorders · 2024Article
- Deep-learning model for predicting physical fitness in possible sarcopenia: analysis of the Korean physical fitness award from 2010 to 2023.Frontiers in public health · 2023Article
- Relationship between age and handgrip strength: Proposal of reference values from infancy to senescence.Frontiers in public health · 2022Article
- Machine Learning-Based Intelligent Scoring System for English Essays under the Background of Modern Information Technology.Computational intelligence and neuroscience · 2022Article
- Prediction and comparison of postural discomfort based on MLP and quadratic regression.Journal of occupational health · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The objective of this study was to accurately predict the grip strength using a deep learning-based method (e.g., multi-layer perceptron [MLP] regression). The maximal grip strength with varying postures (upper arm, forearm, and lower body) of 164 young adults (100 males and 64 females) were collected. The data set was divided into a training set (90% of data) and a test set (10% of data). Different combinations of variables including demographic and anthropometric information of individual participants and postures was tested and compared to find the most predictive model. The MLP regression and 3 different polynomial regressions (linear, quadratic, and cubic) were conducted and the performance of regression was compared. The results showed that including all variables showed better performance than other combinations of variables. In general, MLP regression showed higher performance than polynomial regressions. Especially, MLP regression considering all variables achieved the highest performance of grip strength prediction (RMSE = 69.01N, R = 0.88, ICC = 0.92). This deep learning-based regression (MLP) would be useful to predict on-site- and individual-specific grip strength in the workspace to reduce the risk of musculoskeletal disorders in the upper extremity.
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Registered trials
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