Wearable Smart Sensors for Health Security in Transport: The Case of Study of Diabetic Risk Management thought Advanced Data Analysis Approaches Integrated into Enterprise Process Models

Nicola Magaletti, Gabriele Cosoli and Alessandro Massaro

Abstract

The paper is focused on a pilot case of study about the implementation of smart health sensors in the public transportation sector. The case of study involves business processes of different companies working in transport services, garment manufacturing, and smart health Internet of Things (IoT) sensors. Specifically, the proposed work aims to prove how risk management can be controlled through Personal Protective Equipment (PPE) connected to a control room platform. The new risk management process is executed by means of a platform collecting driver data. The mapping of “AS IS” and the “TO BE” processes by means of the Business Process Modeling Notation (BPMN) approach, highlights the improvement of the procedures applied to predict the health risk, by enabling production and monitoring processes. All processes are described by a platform data flow represented by the Unified Modeling Language (UML) Use Case Diagram (UCD) diagram. Digital data is collected into a data warehouse enabling health monitoring processes. As far as concerns the specific risk addressed by this study the models analyzed in this paper are based on algorithms such as Autoregressive Integrated Moving Average (ARIMA) and Long Short Term Memory (LSTM), both able to predict health status and dangerous conditions of the drivers such as hypo- and hyper- glycemia for Diabetes Mellitus cases. The case study has been developed within the framework of Smart District 4.0 (SD 4.0) project.

Keywords

References

A. Massaro, S. Selicato, and A. Galiano, “Predictive Maintenance of Bus Fleet by Intelligent Smart Electronic Board Implementing Artificial Intelligence”, MDPI IoT, vol. 1, no. 2, (2020), pp. 180-197. https://doi.org/10.3390/iot1020012

S. S. Binyamin, and M. R. Hoque, “Understanding the Drivers of Wearable Health Monitoring Technology: An Extension of the Unified Theory of Acceptance and Use of Technology. Sustainability, vol. 12, no. 9605 (2020). https://doi.org/10.3390/su12229605

J. Chang, W. Yao, and X. Li, “A Context-Aware S-Health Service System for Drivers”, Sensors, vol. 17, no. 609, (2017). https://doi.org/10.3390/s17030609

F. H. Cherif, L. H. Cherif, M. Benabdellah, and G. Nassar, “Monitoring Driver Health Status in Real Time,” Review of Scientific Instruments, vol. 91, no. 035110, (2020). https://doi.org/10.1063/1.5098308

A. Massaro, S. Selicato, G. Ricci, A. Galiano, and S. Raminelli, “Decisional Support System with Artificial Intelligence Oriented on Health Prediction Using a Wearable Device and Big Data”, IEEE Proceeding of MetroInd4.0&IoT, (2020), ISBN: 978-1-7281-4891-5, pp. 718-723. https://doi.org/10.1109/MetroInd4.0IoT48571. 2020.9138258

A. Galiano, A. Massaro, B. Boussahel, D. Barbuzzi, F. Tarulli, L. Pellicani, L. Renna, A. Guarini, G. De Tullio, G. Nardelli, R. Bonaduce, C. Minoia, S. Ciavarella, V. De Fazio, A. Negri, C. Marchionna “Improvements in Haematology for Home Health Assistance and Monitoring by a Web based Communication System”, Proceeding of IEEE International Symposium on Medical Measurements and Applications (MeMeA), (2016), 15-18 May 2016. https://doi.org/10.1109/MeMeA.2016.7533762

A. Massaro, V. Maritati, N. Savino, A. Galiano, D. Convertini, E. De Fonte, and M. Di Muro, “A Study of a Health Resources Management Platform Integrating Neural Networks and DSS Telemedicine for Homecare Assistance”, Information, vol. 9, no. 176, (2018), pp. 1-20, https://doi.org/10.3390/info9070176

A. Massaro, V. Maritati, N. Savino, and A. Galiano, “Neural Networks for Automated Smart Health Platforms oriented on Heart Predictive Diagnostic Big Data Systems,” IEEE Proceeding AEIT, (2018). https://doi.org/10.23919/AEIT.2018.8577362

J.F. De Canete, S. Gonzalez-Perez, and J. Ramos-Diaz, “Artificial Neural Networks for Closed Loop Control of in Silico and ad Hoc Type 1 Diabetes”, Comput. Methods Programs Biomed., vol. 106, no. 1, (2012), pp. 55–66. https://doi.org/10.1016/j.cmpb.2011.11.006

M. Eren-Oruklu, A. Cinar, D.K. Rollins, and L. Quinn, “Adaptive System Identification for Estimating Future Glucose Concentrations and Hypoglycemia Alarms”, Automatica, (2012), vol. 48, no. 8, pp. 1892–1897. https://doi.org/10.1016/j.automatica.2012.05.076

A. Gani, A.V. Gribok, Y. Lu, W.K. Ward, R.A. Vigersky, and J. Reifman, “Universal Glucose Models for Predicting Subcutaneous Glucose Concentration in Humans”, IEEE Trans. Inf. Technol. Biomed., (2009), vol. 14, no. 1, pp. 157–165. https://doi.org/10.1109/titb.2009.2034141

K. Lunze, T. Singh, M. Walter, M. D. Brendel and S. Leonhardt, Steffen. (2013). “Blood Glucose Control Algorithms for Type 1 Diabetic Patients: a Methodological Review”, Biomedical Signal Processing and Control., vol. 8., no. 2, (2013), pp. 107–119. https://doi.org/10.1016/j.bspc.2012.09.003

J. Martinsson, A. Schliep, B. Eliasson, et al. “Blood Glucose Prediction with Variance Estimation Using Recurrent Neural Networks”, J. Healthc. Inform. Res., vol. 4, (2020), pp. 1–18. https://doi.org/10.1007/s41666-019-00059-y

S. M. Pappada, B. D. Cameron, P. M. Rosman, R. E. Bourey, T. J. Papadimos, W. Olorunto, and M. J. Borst, “Neural Network-Based Real-Time Prediction of Glucose in Patients with Insulin-Dependent Diabetes”, Diabetes Technol. Ther., vol. 13, no. 2, (2011), pp. 135–141. https://doi.org/10.1089/dia.2010.0104

S. Shanthi, and Kumar, “A Novel Approach for the Prediction of Glucose Concentration in Type 1 Diabetes Ahead in Time Through ARIMA and Differential Evolution”, Elixir Adv. Engg. Info., vol. 38, (2011), pp. 4182-4186.

T. Singye, and S. Unhapipat, “Time Series Analysis of Diabetes Patients: a Case Study of Jigme Dorji Wangchuk National Referral Hospital in Bhutan”, Journal of Physics: Conference Series (JPCS), vol. 1039, (2018). http://dx.doi.org/10.1088/1742-6596/1039/1/012033

A. Massaro, A., V. Maritati, D. Giannone, D. Convertini, and A. Galiano, “LSTM DSS Automatism and Dataset Optimization for Diabetes Prediction”, Applied Sciences, vol. 9, no.17, (2019), pp. 1-22. https://doi.org/10.3390/app9173532

F. Dubosson, J.-E. Ranvier, S. Bromuri, J.-P. Calbimonte, J. Ruiz, and M. Schumacher, “The Open D1NAMO Dataset: a Multi-Modal Dataset for Research on Non-Invasive Type 1 Diabetes Management”, Informatics in Medicine Unlocked, vol. 13, (2018), pp. 92-100. https://doi.org/10.1016/j.imu.2018.09.003

A. Massaro, “Electronic in Advanced Research Industry: From Industry 4.0 to Industry 5.0 Advances” Wiley/IEEE, (2021), ISBN: 9781119716877. https://books.google.it/books?id=LP5FEAAAQBAJ

A. Massaro, N. Magaletti, V. Giardinelli, G. Cosoli, A. Leogrande, and F. Cannone, “Original Data Vs High Performance Augmented Data for ANN Prediction of Glycemic Status in Diabetes Patients”, (2022). Available at SSRN: https://ssrn.com/abstract=4082839 or http://dx.doi.org/10.2139/ssrn.4082839

Massaro, A., Magaletti, N., Cosoli, G., Cannone, F., and A. Leogrande, A. Use of Machine Learning to Predict the Glycemic Status of Patients with Diabetes, in Proceedings of the 2nd International Electronic Conference on Healthcare, 17 February–3 March 2022, MDPI: Basel, Switzerland, doi:10.3390/IECH2022-12293

A. Massaro, "Information Technology Infrastructures Supporting Industry 5.0 Facilities," in Electronics in Advanced Research Industries: Industry 4.0 to Industry 5.0 Advances, IEEE, (2022), pp. 51-101, doi: 10.1002/9781119716907.ch2.

A. Massaro et al., "Telemedicine DSS-AI Multi Level Platform for Monoclonal Gammopathy Assistance," 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 2020, pp. 1-5, doi: 10.1109/MeMeA49120.2020.9137224.

G.R. Shakhmametova, N.I. Yusupova, V.V. Mironov, and R.Kh. Zulkarneev, “Statistical and Intelligent Methods of Medical Data Processing”, IT in Industry, vol. 6, no.2, (2018), pp. 13-18.

A. Rakibul, P. Bruce Poon, K. M. Mahmood, M. Amin, and Hong Yan, “Annotating Retina Fundus Images for Teaching and Learning Diabetic Retinopathy Software”, IT in Industry, vol. 2, no. 2, (2014), pp. 62-67.

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