Respiration Rate Estimation Based on Independent Component Analysis of Accelerometer Data: Pilot Single-Arm...

Area of Science:

• Biomedical Engineering
• Mobile Health Technology
• Signal Processing

Background:

• Continuous respiration monitoring is crucial but faces accessibility challenges with specialized sensors.
• Noncontact respiration measurement in mobile settings is susceptible to environmental noise.
• Existing methods lack convenience for general users.

Purpose of the Study:

• To develop a method for estimating respiration rate using smartphone accelerometer sensors.
• To provide an accessible and user-friendly approach for respiration monitoring.

Main Methods:

• Acquired data from smartphone accelerometer sensors.
• Utilized independent component analysis for accelerometer calibration.
• Applied quefrency selection to estimate respiration rate based on harmonic components.

Main Results:

• Correlation of 0.7 between smartphone accelerometer and chest belt respiration measurements.
• Statistically insignificant difference (P = 0.27) in respiration counts between the two methods.
• Bland-Altman analysis showed a mean difference of 0.43 breaths per minute within 95% limits of agreement.

Conclusions:

• Smartphone accelerometer-based respiration rate estimation is comparable to traditional chest belts.
• This method overcomes the inconvenience of chest strap attachment and setup.
• Potential application in sleep apnea detection via continuous respiration monitoring in IoT environments.