# Prediction of Three-Dimensional Ground Reaction Forces in the Golf Swing Using Wearable Inertial Measurement Units and Biomimetic Deep Learning Models

## TL;DR

This study shows how wearable sensors and deep learning can predict forces on the ground during golf swings, enabling field-based performance analysis.

## Contribution

The study introduces a novel biomimetic deep learning model for predicting three-dimensional ground reaction forces during complex sports movements using wearable sensors.

## Key findings

- The TCN-BiGRU model achieved high accuracy in predicting ground reaction forces during golf swings.
- Using full bilateral lower-limb sensor configurations provided the best performance, while using only the lead leg remained cost-efficient.
- Vertical ground reaction forces were predicted most reliably among the three directions.

## Abstract

Ground reaction force (GRF) is essential for maintaining dynamic stability and generating power during the golf swing. Traditional GRF assessment relies on force plates, limiting measurement to laboratory environments and restricting evaluation of natural, field-based performance. Recent work has explored wearable inertial measurement units (IMUs) and data-driven models to estimate GRF during simple locomotor tasks, yet no study has examined whether coupled lower-limb kinematics can predict three-dimensional GRF during complex, high-speed movements such as the golf swing. This study collected bilateral hip, knee, and ankle joint angles from IMUs, along with 3D GRF data, to evaluate five biomimetic deep learning (DL) architectures across seven sensor configurations. The TCN-BiGRU model achieved the highest accuracy (R2 = 0.94 ± 0.02, MRE = 0.044 ± 0.01, NRMSE = 0.064 ± 0.01) among the architectures evaluated in this study, effectively capturing both local and long-range temporal dependencies in human movement. The full bilateral lower-limb configuration yielded the best overall performance, whereas using only the lead leg provided a cost-efficient alternative with minimal loss of accuracy. Among the GRF components, the vertical direction showed the greatest predictive reliability. These findings demonstrate the feasibility and potential of kinematic–force modeling and support the development of wearable, field-ready systems for GRF estimation in dynamic sports environments.