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Updated: Jan 10, 2026

Long-term Behavioral Tracking of Freely Swimming Weakly Electric Fish

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Towards Explainable Multimodal Sensing for Swimming Analysis: Early Findings from the SWIM-360 Project.

Vanessa Camilleri¹, Reno Yuri Camilleri¹, Mark Fialovszky¹

¹Department of AI, University of Malta, MSD 2080 Msida, Malta.

Sensors (Basel, Switzerland)

|November 27, 2025

Summary

This summary is machine-generated.

Explainable AI (XAI) can enhance swimming performance analysis by making sensor data interpretable for coaches. The SWIM-360 project develops a framework for actionable insights from multimodal sensing systems.

Keywords:

artificial intelligence (AI)explainable artificial intelligence (XAI)human–computer interaction multimodal sensing sensor technology swimming analysis video-based pose estimation wearable sensors

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Area of Science:

Sports Science
Artificial Intelligence
Human-Computer Interaction

Background:

Swimming performance analysis relies on multimodal sensing systems for physiological and biomechanical data.
Current systems lack interpretability, hindering adoption in coaching.
The SWIM-360 project addresses this gap by exploring explainable AI (XAI).

Purpose of the Study:

To investigate how XAI can provide transparent and actionable insights for swimming performance analysis.
To develop a coach-centered explainability framework based on user requirements.
To explore the feasibility and challenges of translating sensor data into interpretable knowledge.

Main Methods:

Utilized preliminary results from EO SwimBETTER and TrainRed sensors, and video-based pose estimation.
Designed mock-up visualizations and interaction concepts for coach feedback.
Conducted a qualitative questionnaire with eight professional swimming coaches to elicit explainability requirements.

Main Results:

Developed a methodological framework combining multimodal sensing with explainability-driven design principles.
Identified requirements for explainable feedback through coach consultations.
Demonstrated the feasibility and challenges in translating sensor data into interpretable coaching knowledge.

Conclusions:

XAI principles can guide the creation of trustworthy, coach-centered decision-support tools in aquatic sports.
An explainability-driven design framework linking multimodal sensing and user requirements is proposed.
Embedding explainability early in the design process is crucial for practical application.