A dynamic reconfigurable wearable device to acquire high quality PPG signal and robust heart rate estimate based on deep learning algorithm for smart healthcare system

Photoplethysmography (PPG) is a noninvasively technique used to detect vital signs such as heart rate (HR), saturation of peripheral oxygen (SpO2) and blood pressure (BP). Recently, wearable finger-type PPG devices are increasingly developed toward convenience of person under monitoring (PUM). Two most critical features of wearable PPG devices are high accuracy and long operation time. To enhance these functions, this paper proposes a new architecture to select, process and transfer only high quality PPG signals. Hence, data quality is significantly improved and power consumption on wireless module is minimized. In the proposed architecture, parameters of PPG sensor are reconfigured in real-time being suitable with skin characteristics of PUM. Moreover, the adaptive LED current control algorithm is proposed to dynamically change the LED current to remove various motion artifacts (MA) and get high amplitude PPG signal. We also develop a heart rate (HR) estimation framework utilizing deep learning (DL) model based on convolutional neural network (CNN) and long short-term memory (LSTM) network. The combination of CNN-LSTM can extract features from both spatial and temporal correlation. A light-weight model with two CNN-LSTM layers is built to estimate HR with in only 5s. For validation, we conduct experiments with volunteers doing various physical exercises. The results show PPG signals with high amplitude and signal-to-noise ratio (SNR). HR estimation is more accurate even during irregular and muscle strength exercises. The proposed adaptive architecture and DL-based HR estimation can overcome MA and minimize the power consumption on wireless transfer module of wearable PPG devices.