Smartwatch data from non-competitive athletes during the GiroE-2024 e-bike multi-stage race

TL;DR

This paper presents raw data collected from smartwatches worn by athletes during an e-bike race, offering insights into cyclist behavior and heart rate.

Contribution

The study provides a novel dataset of high-frequency sensor data from smartwatches during a real-world e-bike race.

Findings

Raw data from multiple sensors (GNSS, heart rate, altimeter, etc.) was collected at maximum sampling frequency.

The dataset supports research on cyclist behavior, road conditions, and heart rate modeling.

The data can serve as a reference for clinical trials and smartwatch data integration.

Abstract

This article presented the data collected with 9 Garmin Fēnix® 7 - Standard Edition smartwatches during the GiroE-2024. GiroE is an annual e-bike event that runs concurrently with the Giro d'Italia, one of the most prestigious and challenging road cycling races worldwide. Participants ride along selected stages of the Giro d'Italia route, enjoying the same terrains and atmosphere on pedal-assisted bicycles.

The data was collected with an ad-hoc application installed on each smartwatch, purposely developed to log raw data from all available sensors at the maximum sampling frequency possible. The resulting dataset contains raw data from location sensor (based on global navigation satellite system GNSS), heart rate sensor (using photoplethysmography -PPG- and taking advantage from the Garmin Elevate proprietary technology), barometric altimeter, digital compass, tri-axial accelerometer, and thermometer.

The data was collected during the programme ‘Strengthening of research structures and creation of national R&D champions on certain Key Enabling Technologies’ by the ‘National Centre for Sustainable Mobility', Spoke N° 5 - ‘Light Vehicle and Active Mobility’, funded by the Italian Ministry of University and Research with European Union “Next Generation EU” funds.

Therefore, the dataset presented in this article provides raw data supporting research on cyclist behaviour, road conditions, and heart rate modelling. It could be useful in assessing smartwatch data integration and can provide a ground truth for clinical trials made in similar conditions, acting as a “normal” condition for further reference.

Figures