Unconstrained Monitoring Method for Heartbeat Signals Measurement using Pressure Sensors Array

• Main Authors: Yongxiang Jiang, Sanpeng Deng, Hongchang Sun, Yuming Qi
• Format: Article
• Language: English
• Published: MDPI AG 2019-01-01
• Series: Sensors
• Subjects: unconstrained heartbeat signal extraction; pressure sensors array; extreme learning machine (ELM); correlation coefficient
• Online Access: http://www.mdpi.com/1424-8220/19/2/368

An unconstrained monitoring method for a driver’s heartbeat is investigated in this paper. Signal measurement was carried out by using pressure sensors array. Due to the inevitable changes of posture during driving, the monitoring place for heartbeat measurement needs to be adjusted accordingly. An experiment was conducted to attach a pressure sensors array to the backrest of a seat. On the basis of the extreme learning machine classification method, driving posture can be recognized by monitoring the distribution of pressure signals. Then, a band-pass filter in heart rate range is adapted to the pressure signals in the frequency domain. Furthermore, a peak point array of the processed pressure frequency spectrum is derived and has the same distribution as the pressure signals. Thus, the heartbeat signals can be extracted from pressure sensors. Then, the correlation coefficient analysis of heartbeat signals and electrocardio-signals is performed. The results show a high level of correlation. Finally, the effects of driving posture on heartbeat signal extraction are discussed to obtain a theoretical foundation for measuring point real-time adjustment.