IoT Assisted Real Time PPG Monitoring System for Health Care Application

Abstract

Photoplethysmography is an important area that measures heart rate and its variability for clinical diagnosis of cardiac illness and oxygen saturation level in blood. Nowadays biomedical signal transmission through IoT cloud provides an additional benefit in health monitoring especially for ailing senior citizens who are remotely located. The process of bioelectric signal transmission takes place at a very slow sampling rate. In the present work, a prototype system is proposed for PPG monitoring using Internet-of-Things (IoT). PPG data are captured by a reflectance-type PPG sensor with an embedded controller over a measured interval of time. PPG waveforms are then modeled using either Fourier or Gaussian method, the model parameters thus obtained are truly representing the sampled PPG Data. The computed model coefficients are then transmitted to the IoT cloud server (e.g. Dropbox) with WiFi connectivity. At the remote end, provision is made to access these model parameters from the cloud server and reconstructing the PPG waveform. The performance of the reconstruction process is evaluated by calculating mean square error (MSE) and percentage root mean squared difference (PRD). Experiments were performed on ten volunteers of different ages in order to assess the reliability of the entire method. Experimental results reveal the ruggedness of the proposed method, which can supplement the clinical diagnosis in cardiac ailments and facilitate the treatment of rural patients from any urban location through expert physicians.