A study found that human breathing patterns are unique enough to identify individuals with 97% accuracy using machine learning, and these patterns may also provide insights into mental and physical health, potentially aiding future diagnostic tools.
New research shows that individual breathing patterns, or 'breathing fingerprints,' can identify people with 96.8% accuracy and reflect their physical and mental health traits, opening possibilities for health monitoring and treatment through long-term nasal airflow analysis.
Researchers have discovered that each person has a unique 'breath print' based on their breathing patterns, which can identify individuals with over 90% accuracy and may also provide insights into mental health traits, raising potential privacy issues.