A hybrid model for detecting motion artifacts in ballistocardiogram signals.

Journal: Biomedical engineering online
Published Date:

Abstract

BACKGROUND: The field of contactless health monitoring has witnessed significant advancements with the advent of piezoelectric sensing technology, which enables the monitoring of vital signs such as heart rate and respiration without requiring direct contact with the subject. This is especially advantageous for home sleep monitoring, where traditional wearable devices may be intrusive. However, the acquisition of piezoelectric signals is often impeded by motion artifacts, which are distortions caused by the subject of movements and can obscure the underlying physiological signals. These artifacts can significantly impair the reliability of signal analysis, necessitating effective identification and mitigation strategies. Various methods, including filtering techniques and machine learning approaches, have been employed to address this issue, but the challenge persists due to the complexity and variability of motion artifacts.

Authors

  • Yuelong Jiang
    Information Engineering Institute, Guangzhou Railway Polytechnic, Guangzhou 510430, China.
  • Han Zhang
    Johns Hopkins University, Baltimore, MD, USA.
  • Qizheng Zeng
    School of Electronics Science & Engineering (School of Microelectronics), South China Normal University, Foshan, 528200, China.