# Visualizing Relaxation in Wearables: Multi-Domain Feature Fusion of HRV Using Fuzzy Recurrence Plots

## TL;DR

This paper introduces a new way to visualize heart rate variability using images, enabling accurate and easy monitoring of relaxation states through wearable devices.

## Contribution

A novel method using fuzzy recurrence plots (FRPs) to convert HRV data into images and achieve high classification accuracy with minimal features.

## Key findings

- A model using fuzzy recurrence plots achieved 96.6% accuracy in classifying relaxation states.
- The method combines features from five domains and uses SVM for high performance with only three selected features.
- FRPs provide interpretable visual feedback and are suitable for real-time wearable integration.

## Abstract

What are the main findings?
A novel method was developed to convert HRV time series into textured images using fuzzy recurrence plots (FRPs) based on fuzzy set theory.The model achieved 96.6% classification accuracy for relaxation states using only three selected features across multiple domains.

A novel method was developed to convert HRV time series into textured images using fuzzy recurrence plots (FRPs) based on fuzzy set theory.

The model achieved 96.6% classification accuracy for relaxation states using only three selected features across multiple domains.

What are the implications of the main findings?
The model enables both visual and automated interpretation of physiological changes during relaxation, enhancing transparency and user engagement.The model is suitable for real-time integration into low-power wearable devices for stress monitoring and biofeedback.

The model enables both visual and automated interpretation of physiological changes during relaxation, enhancing transparency and user engagement.

The model is suitable for real-time integration into low-power wearable devices for stress monitoring and biofeedback.

Traditional relaxation techniques such as meditation and slow breathing often rely on subjective self-assessment, making it difficult to objectively monitor physiological changes. Electrocardiograms (ECG), which are commonly used by clinicians, provide one-dimensional signals to interpret cardiovascular activity. In this study, we introduce a visual interpretation framework that transforms heart rate variability (HRV) time series into fuzzy recurrence plots (FRPs). Unlike ECGs’ linear traces, FRPs are two-dimensional images that reveal distinctive textural patterns corresponding to autonomic changes. These visually rich patterns make it easier for even non-experts with minimal training to track changes in relaxation states. To enable automated detection, we propose a multi-domain feature fusion framework suitable for wearable systems. HRV data were collected from 60 participants during spontaneous and slow-paced breathing sessions. Features were extracted from five domains: time, frequency, non-linear, geometric, and image-based. Feature selection was performed using the Fisher discriminant ratio, correlation filtering, and greedy search. Among six evaluated classifiers, support vector machine (SVM) achieved the highest performance, with 96.6% accuracy and 100% specificity using only three selected features. Our approach offers both human-interpretable visual feedback through FRP and accurate automated detection, making it highly promising for objectively monitoring real-time stress and developing biofeedback systems in wearable devices.