Mindfulness Meditation and Respiration: Accelerometer-Based Respiration Rate and Mindfulness Progress Estimation to Enhance App Engagement and Mindfulness Skills
arXiv Preprint Archive – July 23, 2025
Source: arXiv
Summary
Many struggle to stick with mindfulness apps. What if your phone could guide you better? Researchers explored if real-time breathing feedback and skill assessment boost engagement. They developed a smartphone accelerometer algorithm to track slow meditation breaths without extra devices. This **cs.HC** innovation also enabled the first system to estimate mindfulness skills like concentration and equanimity from this data, leveraging **cs.LG** for progress estimation. Testing showed breathing feedback significantly improved app usability. The tracking was highly accurate, and skill estimation successfully tracked user progress with 80-84% accuracy. This integration powerfully enhances digital mindfulness training.
Abstract
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.