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.
Highlights
H UMAN skin includes three main depending layer:, the fatty subcutaneous layer, the overlying dermis and the epidermis
The rest of this manuscript is structured as follows:- Section II describes the overall structure of the system model, Section III describes the transmitting near field communication (NFC) smartphone antenna and the receiving skincare device antenna, Section IV provides information regarding to the developed Android application and the energy harvesting performed by the skincare device, Section V provides the experimental results, Section VI introduces the deep learning mechanism used for output prediction and data classification, and section VII
The experimental results of the indoor test indicated that, in most of the subjects, skin moisture was high during the morning and reduced in the evening; the results were quite different in the case of the outdoor environment
Summary
H UMAN skin includes three main depending layer:, the fatty subcutaneous (hypodermis) layer, the overlying dermis and the epidermis. Artificial neural network (ANN) models have been developed a long time ago [44], so in this manuscript, we utilized an ANN model with two hidden layers to process the 6 channel output obtained from the sensors, which was found to be robust and able to efficiently predict the corresponding outputs The rest of this manuscript is structured as follows:- Section II describes the overall structure of the system model, Section III describes the transmitting NFC smartphone antenna and the receiving skincare device antenna, Section IV provides information regarding to the developed Android application (app) and the energy harvesting performed by the skincare device, Section V provides the experimental results, Section VI introduces the deep learning mechanism used for output prediction and data classification, and section VII.
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