Modeling of Wearable Sensor in Various Temperature and Humidity Conditions by Artificial Neural Networks

In this study, the behavior of the sensor printed on the textile surface with carbon nanotube (CNT)-based ink formulated for wearable sensor applications against temperature and humidity was modeled using artificial neural networks. While humidity and temperature are defined as network input variables, the linear electrical resistance value is defined as network output variable. In the study, 167 experimental results were entered as data set, 70% of them were used for ANN training, 15% for validation of the proposed model, and 15% for testing. Levenberg Marquardt (LM) and Bayesian Regularization (BR) were used as the learning algorithm. The logarithmic sigmoid has been used in hidden layers and fitnet in output neurons have been used as an activation function. It has been observed that the developed artificial neural network model exhibits a significant performance in estimating the electrical resistance value against temperature for textile-based sensors developed in different humidity conditions from 50 % relative humidity to 80 % relative humidity and a good agreement with experimental data.