Implementation of Glove-Type Wearable Healthcare System for Heartrate Measurement During Daily Life

Abstract

Background/Objectives: In recently, U-Healthcare has evolved into Smart-Healthcare due to growth of IoT technology and entry into an aging society; it is able to provide variety of medical service technologies. Wearable technology for health monitoring in everyday life is provided with a variety of medical service models through linkage with existing ICT infrastructures.
Method/Statistical Analysis: In the proposed system is first to filter and amp the data measured thought the PPG sensor in the measurement section, and then to perform the ADC conversion in the control section. Then, heart rate was detected from the filtered data by the IBI method after remove the motion noise using the adaptive filter. Finally, heart rate display is possible on the LCD in the control section and transmitted to the monitoring section through Bluetooth communication.
Findings: To evaluate overall performance of implemented system, experiment was conducted to comparative evaluation with commercial system, adaptive filter evaluation and performance evaluation in daily life. For a reliable system evaluation, we conducted a comparative experiment with the commercial system (PSL-iPPG2) of Physio lab company and confirmed the similarity of 98.44%. Evaluated the adaptive filtering implemented by adding artificial noise to the original signal, and it was confirmed that the motion noise remove performance is excellent. In addition, heart rate detection precise of the detection system using the general detection method and the adaptive filter was evaluated. As a result, heart rate detection of 89.19% was found to be low in the case of the general detection method and 98.02% in the case of the heart rate detection method using the adaptive filter.
Improvements/Applications: The implemented system designed an adaptive filter for motion noise remove for precise heart rate measurement in daily life. In addition, confirmed that the system implemented in this paper can be applied to everyday life through experiments. In future research, it is aimed to implement a heart rate monitoring system optimized for motion noise for measuring heart rate with various activity states and psychological changes.