New Directions for Skincare Monitoring: An NFC-Based Battery-Free Approach Combined With Deep Learning Techniques

Abstract

Skincare monitoring has always been of paramount importance in the field of dermatology. In this study, we developed a smart skincare device that can harvest energy from a near field communication (NFC)-based smartphone and allow the adoption of a battery-free design approach. This device consists of two integrated sensors: one for the measurement of skin moisture and another for that of ultraviolet (UV) radiations. We conducted a series of experimental tests on different subjects in indoor and outdoor environments (8 and 6, respectively). Their skin moisture and temperature were measured parallelly to the ultraviolet A (UVA) and ultraviolet B (UVB) radiations from the sun. Later, the 6 channel sensing outputs obtained from the sensors (including ambient humidity and temperature) were input into a deep learning artificial neural network (ANN) model, which was used to predict the corresponding outputs and calculate the respective mean square error (MSE). The ultraviolet index (UVI) outputs were classified (using the same ANN model) into “less harmful”, “moderate harmful” and “burn”. The overall classification accuracy was 99.8%: the best performance achieved by using an ANN model. Notably, our skincare device is enclosed in a 3D flexible design print and is smart, battery-free, equipped with an Android application interface and more convenient to transport than other commercially available devices.

Keywords

• Artificial neural network model
• near field communication
• skin moisture
• skincare
• UVI