# Mindfulness Meditation and Respiration: Accelerometer-Based Respiration Rate and Mindfulness Progress Estimation to Enhance App Engagement and Mindfulness Skills

arXiv: https://arxiv.org/abs/2507.17688 · HTML: https://mlsanity.com/paper/2507.17688/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 108 references that are in the map.
- First posted on arXiv 23 Jul 2025.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Contrastive Predictive Coding for Human Activity Recognition](https://mlsanity.com/paper/2012.05333/) (2020)
- [Estimating Respiratory Rate From Breath Audio Obtained Through Wearable Microphones](https://mlsanity.com/paper/2107.14028/) (2021)
- [Breeze: Sharing Biofeedback Through Wearable Technologies](https://mlsanity.com/paper/1802.04995/) (2018)
- [QUADS: QUAntized Distillation Framework for Efficient Speech Language Understanding](https://mlsanity.com/paper/2505.14723/) (2025)
- [Sound Tagging in Infant-centric Home Soundscapes](https://mlsanity.com/paper/2406.17190/) (2024)

## Abstract (arXiv)

Mindfulness training is widely recognized for its benefits in reducing depression, anxiety, and loneliness. With the rise of smartphone-based mindfulness apps, digital meditation has become more accessible, but sustaining long-term user engagement remains a challenge. This paper explores whether respiration biosignal feedback and mindfulness skill estimation enhance system usability and skill development. We develop a smartphone's accelerometer-based respiration tracking algorithm, eliminating the need for additional wearables. Unlike existing methods, our approach accurately captures slow breathing patterns typical of mindfulness meditation. Additionally, we introduce the first quantitative framework to estimate mindfulness skills-concentration, sensory clarity, and equanimity-based on accelerometer-derived respiration data. We develop and test our algorithms on 261 mindfulness sessions in both controlled and real-world settings. A user study comparing an experimental group receiving biosignal feedback with a control group using a standard app shows that respiration feedback enhances system usability. Our respiration tracking model achieves a mean absolute error (MAE) of 1.6 breaths per minute, closely aligning with ground truth data, while our mindfulness skill estimation attains F1 scores of 80-84% in tracking skill progression. By integrating respiration tracking and mindfulness estimation into a commercial app, we demonstrate the potential of smartphone sensors to enhance digital mindfulness training.