Measurement of Cardiac Parameters by Motion Artifacts Free Photoplethysmography Signals

Adaptive notch-filtration to effectively recover photoplethysmographic signals during physical activity

“…Although the acceleration-based adaptive filter could improve the accuracy of HR calculation, such adaptive filters are still dependent on the signal quality of the reference acceleration signal [28]. The previous study [19] had addressed this issue by proposing the bandstop filter based on the 3-axis acceleration reference, the work was mainly proposed to address the motion scenarios with lower intensity. The ANF described in the present study has effectively removed MAs and recovered cleansed PPG signals using a reference signal from the 3-axis accelerometer at different motion intensities.…”

“…The performance of AF is highly associated with the quality of reference signal, i.e., acceleration signal) as is linearly related to the MAs. Such AF is uneasily applied for a real-time MAs cancellation [19].…”

Design and Development of a Wristband for Continuous Vital Signs Monitoring of COVID-19 Patients

“…The As known, the PPG signals can be affected by any voluntary or involuntary subject motions. The authors previously proposed a robust sensor fusion technique (by using of PPG sensor and accelerometer data) to remove motion artifacts from the recorded PPG signals [19]. In this regard, as future work, the accelerometer output signals measured by the wristband will be used to eliminate the motion artifacts in the recorded PPG signals.…”

Rapid Vital Sign Extraction for Real-Time Opto-Physiological Monitoring at Varying Physical Activity Intensity Levels

“…These MAs may be generated by a motion-induced variation in the distance between the optical sensor and skin surface, contact pressure modulation, and by hemodynamic effects [5]. Hence, MA-corrupted PPG signals may significantly reduce the accuracy and stability of physiological monitoring [6]. Several methods have been presented to reduce MAs and extract the desired PPG signal, such as Independent Component Analysis (ICA) [7], Empirical Mode Decomposition (EMD) [8], Fast Fourier Transformation (FFT) [9], and wavelet de-noising approaches [10].…”