Ultra-wideband radar to measure in vivo musculoskeletal forces

TL;DR

This study demonstrates that ultra-wideband radar can non-invasively and accurately estimate in vivo musculoskeletal forces by detecting electromagnetic property changes in muscles during various activities, offering a promising tool for biomechanics and rehabilitation.

Contribution

The paper introduces a novel application of ultra-wideband radar combined with machine learning to measure musculoskeletal forces non-invasively with high accuracy, surpassing previous methods.

Findings

Radar reliably tracks isometric muscle force.

Force estimation models achieve R2>0.984 with errors<3.3%.

Frequency-dependent effects are independent of muscle structure.

Abstract

Accurate measures of musculoskeletal forces are critical for clinicians, biomechanists, and engineers, yet direct measurement is highly invasive and current estimation methods remain limited in accuracy. Here, we demonstrate the application of ultra-wideband radar to non-invasively estimate musculoskeletal forces by measuring changes in the electromagnetic properties of contracting muscles, in muscles with different structural properties, during various static and dynamic conditions, and in the presence of fatigue. First, we show that ultra-wideband radar scans of muscle can reliably track isometric force in a unipennate knee extensor (vastus lateralis) and a bipennate ankle dorsiflexor (tibialis anterior). Next, we integrate radar signals within machine-learning and linear models to estimate musculoskeletal forces during fatiguing isometric, and dynamic knee extension contractions, with exceptional accuracy (test R2>0.984; errors<3.3%). Finally, we identify frequency-dependent effects of musculoskeletal forces on ultra-wideband radar signals, that are independent of physiological and structural features known to influence muscle force. Together, these findings establish ultra-wideband radar as a powerful, non-invasive approach for quantifying in vivo musculoskeletal forces, with transformative potential for wearable assistive technologies, biomechanics, and rehabilitation.