Fuzzy C-Means Clustering and Sonification of HRV Features
This work addresses the challenge of enhancing data interpretation for health monitoring through auditory displays, but it is incremental, building on existing HRV and sonification methods.
The paper tackled the problem of selecting appropriate heart rate variability (HRV) metrics for sonification to improve interpretability, using unsupervised clustering to aid analysis and vocal synthesis for better comprehension, as early steps toward a real-time sound-based biofeedback system.
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.