Pulse Wave Generation Method for PPG by Using Display

Abstract

The extensive research on wearable devices has led to devices with various shapes and mounting locations. Wearable devices are often used to record the user's biometric information, and methods have been proposed to detect physical abnormalities from the acquired data. Among various kinds of biometric data, pulse data has been used in methods such as heart rate monitoring and emotion recognition. The most common type of pulse sensor uses photoplethysmography (PPG), which irradiates a green LED on the skin and measures pulse data from changes in the light reflected from blood vessels. PPG sensors have been implemented in commercially available wearable devices such as smartwatches. However, a PPG sensor requires blood flow for data acquisition, and when a smartwatch is worn on an artificial body part such as a prosthetic hand or a robotic arm, data cannot be acquired because there is no blood flow. In this study, we propose a method that enables a PPG sensor to measure arbitrary pulse data by using a display. If this method is successful, it will enable pulse data measured at the junction of a living limb and an artificial limb to be input to the display; then, a smartwatch attached to the artificial limb will read the same pulse data. In this paper, we focus on the heart rate and report the results of an experiment in which a target heart rate was input and the display was controlled accordingly to determine whether the target heart rate could be obtained by a smartwatch. We implemented a display drawing program and conducted the evaluation with five kinds of smartwatches and four kinds of displays. The results showed that the error between the target heart rate and the heart rate acquired by the smartwatch was within 3 beats per minute (bpm) in many cases when the target heart rate was set to 60-100 bpm. When the target heart rate was set to 40-55 and 105-200 bpm, the heart rate could also be input to the smartwatch with a small error under certain conditions. Moreover, when generated PPG data was imported into heart rate variability (HRV) analysis software, it was recognized as a pulse wave in the same way as real PPG data obtained from a person. We compared the heart rate, RR interval, and SDNN calculated from the real and generated PPG data, and we confirmed that the proposed method could generate stable PPG data. On the other hand, when the waveforms were compared, the generated PPG waveform differed greatly from the real PPG waveform, which indicated that the software could calculate the heart rate, RR interval, SDNN, and LF/HF ratio regardless of the waveform. This result suggests that calculation of these parameters without verifying the waveform would be vulnerable to an attack with fake PPG data.

Real-time heart rate estimation using PVDF flexible piezoelectric sensor based on machine learning

“…If the heart rate is too fast or too slow, it may be a signal of certain cardiovascular diseases or other illnesses. The pulse signal is a pulsating wave formed by blood being pushed into the arteries when the heart contracts, which contains many health information of the human body, is one of the important signals for monitoring heart rate [2], therefore, pulse signal acquisition and analysis can be employed as an indirect means of assessing heart rate.…”