A study on non‐invasive detection of blood glucose concentration from human palm perspiration by using artificial neural networks

Abstract

Abstract: In this paper the relationship between blood glucose concentration and palm perspiration rate is studied as a non‐invasive method. A glucose concentration range from 83 mg/dl to 116.5 mg/dl is examined. An artificial neural network (ANN) trained by the Levenberg–Marquardt algorithm is developed to detect the performance indices based on the one‐ and two‐input variables. A data set for 72 volunteers is used for this study. Data of 36 volunteers are used for training the ANN and data of 36 volunteers were reserved for testing. Results of the study are acceptable with an error of 8.38% for the Elman neural network and 8.77% for the multilayer neural network. Therefore, the palm perspiration rate may be used as a good indicator for detecting glucose concentration in blood. This non‐invasive method has advantages such as time saving, cost etc. over other methods and it is painless. The results of clinical experiments, follow‐up methods and other applications are presented.