TY - JOUR
T1 - Motion Artifact Cancellation of Seismocardiographic Recording from Moving Subjects
AU - Yang, Chenxi
AU - Tavassolian, Negar
N1 - Publisher Copyright:
© 2001-2012 IEEE.
PY - 2016/7/15
Y1 - 2016/7/15
N2 - This paper presents a novel method of extracting seismocardiographic (SCG) data from moving adult subjects recorded via micro-electromechanical (MEMS) accelerometers. A digital signal processing system based on the normalized least mean square (NLMS) adaptive filter design is developed in MATLAB to process the signals collected from the MEMS sensor node. Standardized experiments were performed on 40 moving adult subjects. False-positives were ruled out for a more precise detection rate. The research on sliding ensemble average was also conducted to find the minimum required window size. The results indicate a detection rate of 96% and a sliding window size of 32 intervals for robust continuous monitoring, showing that adaptive filtering could be a promising technique for the cancellation of motion noise artifacts from SCG recordings in moving subjects.
AB - This paper presents a novel method of extracting seismocardiographic (SCG) data from moving adult subjects recorded via micro-electromechanical (MEMS) accelerometers. A digital signal processing system based on the normalized least mean square (NLMS) adaptive filter design is developed in MATLAB to process the signals collected from the MEMS sensor node. Standardized experiments were performed on 40 moving adult subjects. False-positives were ruled out for a more precise detection rate. The research on sliding ensemble average was also conducted to find the minimum required window size. The results indicate a detection rate of 96% and a sliding window size of 32 intervals for robust continuous monitoring, showing that adaptive filtering could be a promising technique for the cancellation of motion noise artifacts from SCG recordings in moving subjects.
KW - Adaptive filtering
KW - LMS filtering
KW - MEMS accelerometer
KW - Motion artifact
KW - Seismocardiography (SCG)
KW - Wearable sensor network
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U2 - 10.1109/JSEN.2016.2573269
DO - 10.1109/JSEN.2016.2573269
M3 - Article
AN - SCOPUS:84976508198
SN - 1530-437X
VL - 16
SP - 5702
EP - 5708
JO - IEEE Sensors Journal
JF - IEEE Sensors Journal
IS - 14
M1 - 7479480
ER -