Unobtrusive Estimation of Cardiovascular Parameters with Limb Ballistocardiography

Area of Science:

• Biomedical Engineering
• Cardiovascular Physiology
• Wearable Technology

Background:

• Cardiovascular (CV) parameters are crucial for health assessment.
• Current methods for measuring CV parameters can be invasive or inconvenient.
• Ballistocardiography (BCG) offers a potential non-invasive alternative.

Purpose of the Study:

• To investigate the potential of limb ballistocardiogram (BCG) for unobtrusive estimation of cardiovascular parameters.
• To compare upper-limb and lower-limb BCG measurements against reference CV parameters.
• To develop a standardized method for analyzing BCG data from wearable sensors.

Main Methods:

• Simultaneous measurement of upper-limb BCG (armband accelerometer), lower-limb BCG (weighing scale strain gauge), and finger photoplethysmogram (PPG).
• Signal processing to transform armband BCG into a synthetic weighing scale BCG.
• Extraction of characteristic features (time intervals, amplitudes) from BCG and PPG waveforms.
• Multivariate linear regression analysis to correlate features with CV parameters (diastolic/systolic/pulse pressures, stroke volume, cardiac output, total peripheral resistance).

Main Results:

• Cardiovascular parameters can be accurately estimated using a combination of as few as two characteristic features from either upper-limb or lower-limb BCG.
• The extracted features showed relevance to the underlying physiological mechanisms of BCG.
• Both direct weighing scale BCG and synthetic BCG (derived from armband BCG) provided accurate estimations when paired with PPG.

Conclusions:

• Limb BCG, particularly from wearable sensors, is a promising non-invasive method for estimating multiple cardiovascular parameters.
• The study validates the use of BCG features for physiological monitoring and suggests potential for remote health assessment.
• Standardized signal processing enables the use of convenient wearable sensors for robust cardiovascular monitoring.