IoT-Inspired Smart Toilet System for Home-Based Urine Infection Prediction

被引：26

作者：

Bhatia M. ^{[1
]}

Kaur S. ^{[2
]}

Sood S.K. ^{[3
]}

机构：

[1] Department of Computer Science and Engineering, Lovely Professional University

[2] Department of Computer Science and Informatics, Central University of Himachal, Pradesh

来源：

ACM Transactions on Computing for Healthcare | 2020年 / 1卷 / 03期

关键词：

Internet of Things; smart toilet system; SOM visualization; temporal prediction;

D O I：

10.1145/3379506

中图分类号：

学科分类号：

摘要：

The healthcare industry is the premier domain that has been significantly influenced by incorporation of Internet of Things (IoT) technology resulting in smart healthcare application. Inspired by the enormous potential of IoT technology, this research provides a framework for an IoT-based smart toilet system, which enables home-based determination of Urinary Infection (UI) efficaciously. The overall system comprises a four-layered architecture for monitoring and predicting infection in urine. The layers include the Urine Acquisition, Urine Analyzation, Temporal Extraction, and Temporal Prediction layers, which enable an individual to monitor his or her health on daily basis and predict UI so that precautionary measures can be taken at early stages. Moreover, probabilistic quantification of urine infection in the form of Degree of Infectiousness (DoI) and Infection Index Value (IIV) were performed for infection prediction based on a temporal Artificial Neural Network. In addition, the presence of UI is displayed to the user based on a Self-Organized Mapping technique. For validation purposes, numerous experimental simulations were performed on four individuals for 60 days. Results were compared with different state-of-the-art techniques for measuring the overall efficiency of the proposed system. © 2020 ACM.

引用

共 65 条

[51]

Sha K., Wei W., Andrew Yang T., Wang Z., Shi W., On security challenges and open issues in internet of things, Future Generation Computer Systems, 83, pp. 326-337, (2018)

[52]

Srinivas J., Kumar Das A., Wazid M., Kumar N., Anonymous lightweight chaotic map-based authenticated key agreement protocol for industrial internet of things, IEEE Transactions on Dependable and Secure Computing, (2018)

[53]

Stergiou C., Psannis K.E., Kim B., Gupta B., Secure integration of iot and cloud computing, Future Generation Computer Systems, 78, pp. 964-975, (2018)

[54]

Sun W., Cai Z., Li Y., Liu F., Fang S., Wang G., Security and privacy in the medical internet of things: A review, Security and Communication Networks, 2018, (2018)

[55]

Tang B., Chen Z., Hefferman G., Pei S., Wei T., He H., Yang Q., Incorporating intelligence in fog computing for big data analysis in smart cities, IEEE Transactions on Industrial Informatics, 13, 5, pp. 2140-2150, (2017)

[56]

Tomar D., Agarwal S., A survey on data mining approaches for healthcare, International Journal of Bio-Science and Bio-Technology, 5, 5, pp. 241-266, (2013)

[57]

Tuli S., Basumatary N., Singh Gill S., Kahani M., Chand Arya R., Singh Wander G., Buyya R., Healthfog: An ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated iot and fog computing environments, Future Generation Computer Systems, 104, pp. 187-200, (2019)

[58]

Uddin Z., A wearable sensor-based activity prediction system to facilitate edge computing in smart healthcare system, Journal of Parallel and Distributed Computing, 123, pp. 46-53, (2019)

[59]

Ultsch A., U-Matrix: A Tool to Visualize Clusters in High Dimensional Data, (2003)

[60]

Wang C., Boyd R., Mitchell R., Darryl Wright W., McCracken C., Escoffery C., Patzer R.E., Greenbaum L.A., Development of a novel mobile application to detect urine protein for nephrotic syndrome disease monitoring, BMC Medical Informatics and Decision Making, 19, 1, (2019)

← 1 2 3 4 5 6 7 →