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A Multi-Modal Dataset for Ground Reaction Force Estimation Using Consumer Wearable Sensors.

Parvin Ghaffarzadeh¹, Debarati Chakraborty², Koorosh Aslansefat²

¹Department of Data Science & Artificial Intelligence and Modelling (DAIM), University of Hull, Hull, UK. p.ghaffarzadeh@hull.ac.uk.

Scientific Data

|April 10, 2026

Summary

This summary is machine-generated.

This study introduces an open dataset for estimating vertical ground reaction forces (vGRF) using Apple Watch sensors. The data enables benchmarking machine learning models for wearable biomechanics research.

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Area of Science:

Biomechanics
Wearable technology
Data science

Background:

Estimating vertical ground reaction forces (vGRF) is crucial for biomechanical analysis.
Consumer-grade wearables offer potential for accessible vGRF measurement.
Existing datasets often lack multi-modal validation or open accessibility.

Purpose of the Study:

To present a comprehensive, open-access, multi-modal dataset for vGRF estimation.
To validate vGRF estimation using consumer-grade Apple Watch sensors against laboratory force plates.
To support the development and benchmarking of machine learning models for wearable biomechanics.

Main Methods:

Collected synchronized data from Apple Watches (wrist and waist) and force plates during five activities (walking, jogging, running, heel drops, step drops).
Recorded inertial measurement unit (IMU) data at ~100 Hz and force plate data at 1000 Hz.
Ensured data integrity through trial-level manifests and performed cross-sensor plausibility checks.

Main Results:

The dataset includes 492 validated trials with high sensor availability (≥91.3%).
80.3% of trials (395) provided complete data across wrist, waist, and force plate sensors.
Repeatability metrics for peak vGRF showed high consistency (ICC 0.871-0.990).

Conclusions:

This open dataset facilitates reproducible research in wearable biomechanics.
It serves as a valuable resource for benchmarking machine learning models for vGRF estimation.
The findings support the use of consumer wearables for biomechanical analysis.