Original Article

Medical & Biological Engineering & Computing

, Volume 49, Issue 2, pp 213-220

First online:

Automatic detection of motion artifacts in the ballistocardiogram measured on a modified bathroom scale

  • Richard M. WiardAffiliated withDepartment of Bioengineering, Stanford University Email author 
  • , Omer T. InanAffiliated withDepartment of Electrical Engineering, Stanford University
  • , Brian ArgyresAffiliated withDepartment of Mechanical Engineering, Stanford University
  • , Mozziyar EtemadiAffiliated withDivision of Pediatric Surgery, University of California, San Francisco
  • , Gregory T. A. KovacsAffiliated withDepartment of Electrical Engineering, Stanford UniversityDepartment of Medicine, Stanford University
  • , Laurent GiovangrandiAffiliated withDepartment of Electrical Engineering, Stanford University

Rent the article at a discount

Rent now

* Final gross prices may vary according to local VAT.

Get Access


Ballistocardiography (BCG) is a non-invasive technique used to measure the ejection force of blood into the aorta which can be used to estimate cardiac output and contractility change. In this work, a noise sensor was embedded in a BCG measurement system to detect excessive motion from standing subjects. For nine healthy subjects, the cross-correlation of the motion signal to the BCG noise—estimated using a simultaneously acquired electrocardiogram and statistics of the BCG signal—was found to be 0.94 and 0.87, during periods of standing still and with induced motion artifacts, respectively. In a separate study, where 35 recordings were taken from seven subjects, a threshold-based algorithm was used to flag motion-corrupted segments of the BCG signal using only the auxiliary motion sensor. Removing these flagged segments enhanced the BCG signal-to-noise ratio (SNR) by an average of 14 dB (P < 0.001). This integrated motion-sensing technique addresses a gap in methods available to identify and remove noise in standing BCG recordings due to movement, in a practical manner that does not require user intervention or obtrusive sensing.


Ballistocardiography Cardiac output Contractility Heart failure Cardiovascular monitoring Signal processing Noise identification