Smart wristband with integrated chemical sensors for detecting glucose levels using breath volatile organic compounds

This paper presents a microcontroller-based solution to classify blood glucose levels using acetone and ethanol breath volatile organic compounds. Two metal oxide semiconductor-based chemical sensors able to detect acetone and ethanol at parts per million concentrations were used. The sensors were tested in a controlled setup with humidified air spiked with acetone and ethanol, mimicking human breath corresponding to low and high blood glucose groups. A support vector machine algorithm was trained and implemented in a microcontroller. In a real time-time test, the trained algorithm classified low and high blood glucose groups with 97% accuracy. Subsequently, a smart wristband prototype that integrates the two sensors was developed. An Arduino-based wearable microcontroller platform was used for its small formfactor and a low-power operation. The wristband is enclosed in a 3D printed housing and powered by an onboard 3.7 V 500 mAh rechargeable Li-ion battery. A smartphone app communicates with the wristband through Bluetooth, allows data visualization, and saves data in the cloud. The presented work makes a significant contribution towards the development of a wearable device for detecting blood glucose levels from a patient's breath.