Author:
Raji Rafiu King,Adjeisah Michael,Miao Xuhong,Wan Ailan
Abstract
Purpose
The purpose of this paper is to introduce a novel respiration pattern-based biometric prediction system (BPS) by using artificial neural network (ANN).
Design/methodology/approach
Respiration patterns were obtained using a knitted piezoresistive smart chest band. The ANN model was implemented by using four hidden layers to help achieve the best complexity to produce an adequate fit for the data. Not only did this study give a detailed distribution of an ANN model construction including the scheme of parameters and network layers, ablation of the architecture and the derivation of back-propagation during the iterations but also engaged a step-based decay to systematically drop the learning rate after specific epochs during training to minimize the loss and increase the model’s accuracy as well as to limit the risk of overfitting.
Findings
Findings establish the feasibility of using respiratory patterns for biometric identification. Experimental results show that, with a learning rate drop factor = 0.5, the network is able to continue to learn past epoch 40 until stagnation occurs which yielded a classification accuracy of 98 per cent. Out of 51,338 test set, the model achieved 51,557 correctly classified instances and 169 misclassified instances.
Practical implications
The findings provide an impetus for possible studies into the application of chest breathing sensors for human machine interfaces in the area of entertainment.
Originality/value
This is the first time respiratory patterns have been applied in biometric prediction system design.
Subject
Electrical and Electronic Engineering,Industrial and Manufacturing Engineering
Reference24 articles.
1. Heart-based biometrics and possible use of heart rate variability in biometric recognition systems,2016
2. ECG analysis: a new approach in human identification;IEEE Transactions on Instrumentation and Measurement,2001
3. EEG biometrics,2009
4. Psychological stress detection using phonocardiography signal: an empirical mode decomposition approach;Biomedical Signal Processing and Control,2019
5. Prognosticating autism spectrum disorder using artificial neural network: Levenberg-Marquardt algorithm;Journal of Bioinformatics and Systems Biology,2018
Cited by
12 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献