TY - GEN
T1 - Improved Heart Rate Tracking Using Multiple Wrist-type Photoplethysmography during Physical Activities
AU - Zhu, Lianning
AU - Du, Dongping
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/26
Y1 - 2018/10/26
N2 - Photoplethysmography (PPG) signals collected from wearable sensing devices during physical exercise are easily corrupted by motion artifact (MA), which poses great challenge on heart rate (HR) estimation. This paper proposes a new framework to accurately estimate HR using two leads of PPG signals in combination with accelerometer (ACC) data in the presence of MA. A moving time window is first used to segment PPG signals and ACC signals. Then, MA is attenuated by joint sparse spectrum reconstruction in each time window, where maximum spectrum frequencies of ACC are subtracted from the spectrum frequency of PPG signals. Further, HR for each cleansed PPG is estimated from the frequency with maximum amplitude in the sparse spectrum. The actual HR is determined using spectral band powers calculated from each reconstructed PPG signals. The proposed method was validated using the 2015 IEEE Signal Processing Cup dataset. The average absolute error is 1.15 beats per minutes (BPM) (standard deviation: 2.00 BPM), and the average absolute error percentage is 0.95% (standard deviation: 1.86%). The proposed method outperforms the previously reported work in terms of accuracy.
AB - Photoplethysmography (PPG) signals collected from wearable sensing devices during physical exercise are easily corrupted by motion artifact (MA), which poses great challenge on heart rate (HR) estimation. This paper proposes a new framework to accurately estimate HR using two leads of PPG signals in combination with accelerometer (ACC) data in the presence of MA. A moving time window is first used to segment PPG signals and ACC signals. Then, MA is attenuated by joint sparse spectrum reconstruction in each time window, where maximum spectrum frequencies of ACC are subtracted from the spectrum frequency of PPG signals. Further, HR for each cleansed PPG is estimated from the frequency with maximum amplitude in the sparse spectrum. The actual HR is determined using spectral band powers calculated from each reconstructed PPG signals. The proposed method was validated using the 2015 IEEE Signal Processing Cup dataset. The average absolute error is 1.15 beats per minutes (BPM) (standard deviation: 2.00 BPM), and the average absolute error percentage is 0.95% (standard deviation: 1.86%). The proposed method outperforms the previously reported work in terms of accuracy.
UR - http://www.scopus.com/inward/record.url?scp=85056614304&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2018.8512736
DO - 10.1109/EMBC.2018.8512736
M3 - Conference contribution
C2 - 30440267
AN - SCOPUS:85056614304
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 1
EP - 4
BT - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
Y2 - 18 July 2018 through 21 July 2018
ER -