A Study on the Correlation Between Hand Grip and Age Using Statistical and Machine Learning Analysis

Aug 4, 2026

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Handgrip strength (HGS) serves as a simple tool for monitoring health status and exploring demographic patterns. This study classifies age groups based on handgrip values using machine learning and statistical testing. Fifty-four participants aged 24–57 were measured three times with a Digital Pinch Grip Analyzer, and results were recorded via Clinical Analysis Software. An independent t-test assessed significant factors, then Support Vector Machine, Random Forest, and Naïve Bayes classified the groups. SVM achieved the highest accuracy (up to 98%).

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Introduction

Background on handgrip strength

Prior studies on handgrip and age across countries

Brazil, Switzerland, Saudi Arabia, and Greece

Malaysia and related measurement practices

Machine learning applications in health-related classification

A Study on the Correlation Between Hand Grip and Age Using Statistical and Machine Learning Analysis Sahnius Usman1 *, Fatin ‘Aliah Rusli1, Nurul Aini Bani1, Mohd Nabil Muhtazaruddin1, Firdaus Muhammad-Sukki2 1Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Kuala Lumpur, 54100, MALAYSIA 2Edinburgh Napier University, Sighthill Campus, Sighthill Court, Edinburgh, EH11 4BN, UNITED KINGDOM *Corresponding Author DOI: [https://doi.org/10.30880/ijie.2023.15.03.008](https://doi.org/10.30880/ijie.2023.15.03.008) Received 31 October 2022; Accepted 29 December 2022; Available online 31 July 2023 Abstract: Handgrip strength (HGS) is an easy-to-use instrument for monitoring people's health status. Numerous researchers in many countries have done a study on handgrip disease or demographic data. This study focused on classifying aged groups referring to handgrip value using machine learning. A total of fifty-four participants had involved in this study, ages ranging from 24 years to 57 years old. Digital Pinch Grip Analyzer had been used to measure the handgrip measurement three times to get more accurate results. The result is then recorded by Clinical Analysis Software (CAS) that is built into the analyzer. An independent t-test is used to investigate the significant factor for age group classification. The data were then classified using machine learning analysis which are Support Vector Machine (SVM), Random Forest (RF), and Naïve Bayes. The overall dataset shows that the Support Vector Machine is the most suitable classification technique with average accuracy between 5 groups of age is 98%, specificity of 0.79, the sensitivity of 0.9814 and 0.0185 of mean absolute error. SVM also give the lowest mean absolute error compared to RF and Naïve Bayes. This study is consistent with the previous work that there is a relationship between handgrip and age. Keywords: Handgrip measurement, machine learning technique, age classification 1. Introduction Handgrip strength appears to be an attractive, easy-to-use instrument for monitoring health status among adults orthe elderly [1]-[10] . It is a simple and fast clinical measurement that has emerged as a proxy assessment of overall muscular strength. HGS also a general indicator of muscle strength linked with premature mortality. Using a handgrip dynamometer, one can measure how much static force their hand can squeeze under standard conditions. Newtons and kilograms are the most prevalent units of measurement for the force. The American Society for Surgery of the Hand and the American Society of Hand Therapists have standardized the posture, instruction, and computation of grip strength of patients during measurements, as there are various techniques [11] . Several studies have been done between handgrip and age in other countries. In Brazil, a group of researchers investigated the effect of gender and age on handgrip strength. According to them, both men and women experienced handgrip declines as they got older. The handgrip strength of men in this demographic peaks around 30 and subsequently declines with age [12] . Werle et al. measured grip and pinch strength in a typical Swiss population and came up with age and gender-specific reference values. In men, handgrip peaks between 35 and 39, while for women, handgrip peaks between 40 and 44 and then decline after that point [13] . Saudi Arabian researchers developed normative values of HGS based on gender and age [14] . HGS was found to be adversely correlated with age for both men and women. However, because the sample was restricted to senior citizens in the Riyadh area, it cannot be applied to the entire Saudi population. They indicated that further research is needed in Saudi Arabia to verify the current findings. Next, a study on the Greek adult population concludes with a negative correlation between age and handgrip strength. According to the researchers, people's HGS declines after age

FAQs

What is the main goal of the study?

To classify age groups using handgrip values through machine learning, supported by statistical analysis of significant factors.

How were handgrip measurements collected?

Handgrip was measured three times per participant using a Digital Pinch Grip Analyzer, with results recorded using Clinical Analysis Software built into the analyzer.

Which machine learning method performed best for age classification?

Support Vector Machine (SVM) showed the highest overall performance, with the best accuracy reported for classifying age groups.

Aug 4, 2026

Upload by Finn