Related Experiment Video
Updated: Jul 16, 2026
08:47
Automated Analysis of C. elegans Swim Behavior Using CeleST Software
Published on: December 7, 2016
Wearable Sensor-Based Phase Segmentation Analysis of Front Crawl Swimming: A Scoping Review.
Jonathan Simoes1, Samuel Aylward1, Daniel Hamze1
1School of Health Science, Oakland University, Rochester, MI 48309, USA.
Sensors (Basel, Switzerland)
|July 15, 2026
Summary
Wearable sensors offer automated front crawl swimming phase segmentation. Current methods use inertial measurement units (IMUs) but lack force data, hindering practical coaching applications.
Area of Science:
- Sports Science
- Biomechanics
- Wearable Technology
Background:
- Traditional front crawl swimming analysis relies on video.
- Wearable sensors enable automated stroke phase segmentation.
- This review focuses on sensor-based methods for front crawl.
Purpose of the Study:
- To map evidence on wearable sensor-based stroke phase segmentation in front crawl swimming.
- To identify common methodologies and research gaps.
- To inform future research directions.
Main Methods:
- Scoping review following PRISMA-ScR guidelines.
- Systematic search of SPORTDiscus, Web of Science, and IEEE Xplore (Jan-Jun 2026).
- Included 15 peer-reviewed studies (2000-2024).
Main Results:
- An emerging research field using inertial measurement units (IMUs).
- Convergence on the Chollet phase segmentation framework.
- Heterogeneity in algorithms and validation; no force/pressure outcomes reported.
Conclusions:
- Current sensor-based segmentation is advancing but lacks crucial biomechanical force data.
- A significant gap exists between segmentation capabilities and applied coaching needs.
- Further research on algorithm validation and intra-phase force measurement is essential.
