# Segmental bioimpedance and anthropometry improve machine learning prediction of grip strength in healthy young adults

## TL;DR

This study shows that combining bioimpedance measurements and body measurements from the forearm can help predict hand grip strength in young adults, which could improve wearable health devices.

## Contribution

The study introduces a novel approach using localized, size-normalized forearm bioimpedance to enhance machine learning predictions of grip strength.

## Key findings

- Forearm bioimpedance values were higher than wrist values and inversely correlated with forearm circumference.
- Incorporating forearm bioimpedance improved grip strength prediction accuracy in machine learning models.
- High-frequency bioimpedance showed linear responses to pressure, while low-frequency responses were non-linear and less stable.

## Abstract

Accurate, non-invasive assessment of muscle strength remains a key challenge for functional health monitoring and wearable systems. This study investigates whether segmental bioimpedance (BioZ) and anthropometry measurements from the wrist and forearm can predict hand grip strength (HGS) in healthy young adults, and characterizes how measurement site, frequency, and applied pressure influence BioZ signal behavior, which are critical factors for translating BioZ into wearable applications.

We recruited twenty healthy young adults who underwent standardized HGS testing alongside segmental BioZ measurements at the wrist and forearm using a bipolar electrode configuration. Anthropometric variables including age, height, body mass, and limb circumference were recorded. Nonparametric statistical analyses were used to examine anatomical site-specific differences and associations among BioZ, circumference, and HGS. Multiple linear regression (MLR) and random forest (RF) regression models were developed to estimate HGS from anthropometric and localized BioZ features and evaluated using leave-one-out cross validation. In addition, exploratory single-subject experiments were conducted to assess BioZ responses to varying frequency, applied pressure, and electrode configuration at both anatomical sites.

At the cohort level, forearm BioZ values were higher than wrist BioZ (p = 0.0001) and inversely correlated with forearm circumference (ρ = −0.54, p = 0.014). Forearm circumference showed the strongest positive association with HGS, while forearm BioZ exhibited a moderate inverse association. Incorporating localized forearm BioZ into baseline improved predictive performance (RF regression: R2
cv = 0.44). A size-normalized BioZ index further enhanced prediction accuracy, achieving the highest in RF regression models (R2
cv = 0.56). Frequency- and pressure-dependent analyses revealed that high-frequency BioZ increased linearly with applied pressure, whereas low-frequency BioZ exhibited non-linear and less stable behavior, suggesting sensitivity to tissue compression and local fluid redistribution.

This pilot study demonstrates that localized, size-normalized forearm BioZ provides physiologically complementary information to basic anthropometry for estimating HGS in healthy young adults. By integrating cohort-level modeling with exploratory mechanistic experiments, the findings provide insight into the anatomical and mechanical determinants of localized BioZ behavior. This supports the potential utility of combining experimental and computational approaches to inform the future development of next-generation BioZ-based wearable systems for non-invasive assessment of muscle strength, rehabilitation progress, and early signs of muscular decline.