Fuzzy C-Means Clustering and Sonification of HRV Features

TL;DR

This paper investigates using fuzzy c-means clustering and vocal sonification of HRV features to enhance real-time biofeedback and interpretability of heart rate variability data for health monitoring.

Contribution

It introduces a novel approach combining unsupervised clustering with vocal sonification to improve HRV data analysis and biofeedback systems.

Findings

Unsupervised clustering identifies relevant HRV features.

Vocal sonification enhances data interpretability.

Early development of real-time sound-based biofeedback system.

Abstract

Linear and non-linear measures of heart rate variability (HRV) are widely investigated as non-invasive indicators of health. Stress has a profound impact on heart rate, and different meditation techniques have been found to modulate heartbeat rhythm. This paper aims to explore the process of identifying appropriate metrices from HRV analysis for sonification. Sonification is a type of auditory display involving the process of mapping data to acoustic parameters. This work explores the use of auditory display in aiding the analysis of HRV leveraged by unsupervised machine learning techniques. Unsupervised clustering helps select the appropriate features to improve the sonification interpretability. Vocal synthesis sonification techniques are employed to increase comprehension and learnability of the processed data displayed through sound. These analyses are early steps in building a real-time sound-based biofeedback training system.