Low-cost and portable physiological signal monitor using PhysRate model

Abstract

In this work, we propose a lightweight non-contact physiological signal monitor. The proposed system is equipped with the modified DeepPhys model and the rate estimation model PhysRate that enable non-contact physiological signal measurements without markers using the low-cost color camera. We introduce the constraint term guided by scene information into the original DeepPhys model to mitigate the overfitting problem of the original DeepPhys model. The results of validation experiments in ICU observation ward show that the modified DeepPhys model can effectively counteract the overfitting problem of the original DeepPhys model. The experimental results of PhysRate indicate that it exhibits certain performance improvements compared to other rate prediction models. In terms of system integration, the proposed physiological signal detection monitor includes Raspberry Pi 4B, RGB camera, 4G module, mobile power, and mobile terminal. With the help of above five modules, the system can analyze the captured signals at the edge and then transmit the processed data to the cloud via 4G network. The edge side is equipped with physiological signal detection model and the other data processing algorithms. On mobile devices, the users can view the relevant data through the developed Mini App. The PhysRate model code is available at https://github.com/ShawnTan86/PhysRate.