TY - GEN
T1 - Non-contact Breathing Rate Detection Based on Time of Flight Sensor
AU - Yang, Chengxu
AU - Huang, Xinxin
AU - Zheng, Yu
AU - Xie, Yufei
AU - Duan, Xiaohui
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - There are a growing number of methods to detect a person's breathing rate, but most techniques still either require contact with body skin or are usually uncomfortable to wear, too expensive and unfriendly for daily monitoring. The massive adoption of smartphones in recent years has created many opportunities to improve daily health monitoring. In this work, we demonstrated that off-the-shelf ToF lens on smartphones can capture a person's breathing rate while still. In addition, we proposed a method for extracting breathing rate from ToF signal and compared it with actual breathing rate obtained from temperature sensor. We evaluated the breathing rate accuracy of 6 people at rest, with a mean absolute error of 0.009Hz when considering different mean breathing rate conditions. Moreover, the mean absolute error percentage is 3.56% and the root mean squared percentage error is 6.64%, which is smaller than other methods of non-contact breathing rate detection in recent works.
AB - There are a growing number of methods to detect a person's breathing rate, but most techniques still either require contact with body skin or are usually uncomfortable to wear, too expensive and unfriendly for daily monitoring. The massive adoption of smartphones in recent years has created many opportunities to improve daily health monitoring. In this work, we demonstrated that off-the-shelf ToF lens on smartphones can capture a person's breathing rate while still. In addition, we proposed a method for extracting breathing rate from ToF signal and compared it with actual breathing rate obtained from temperature sensor. We evaluated the breathing rate accuracy of 6 people at rest, with a mean absolute error of 0.009Hz when considering different mean breathing rate conditions. Moreover, the mean absolute error percentage is 3.56% and the root mean squared percentage error is 6.64%, which is smaller than other methods of non-contact breathing rate detection in recent works.
UR - https://www.scopus.com/pages/publications/85122515607
U2 - 10.1109/EMBC46164.2021.9630819
DO - 10.1109/EMBC46164.2021.9630819
M3 - 会议稿件
C2 - 34892780
AN - SCOPUS:85122515607
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 7284
EP - 7287
BT - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2021
Y2 - 1 November 2021 through 5 November 2021
ER -