publication . Preprint . Conference object . 2017

Smart Fog: Fog Computing Framework for Unsupervised Clustering Analytics in Wearable Internet of Things

Debanjan Borthakur; Harishchandra Dubey; Nicholas Constant; Leslie Mahler; Kunal Mankodiya;
Open Access English
  • Published: 24 Dec 2017
Abstract
The increasing use of wearables in smart telehealth generates heterogeneous medical big data. Cloud and fog services process these data for assisting clinical procedures. IoT based ehealthcare have greatly benefited from efficient data processing. This paper proposed and evaluated use of low resource machine learning on Fog devices kept close to the wearables for smart healthcare. In state of the art telecare systems, the signal processing and machine learning modules are deployed in the cloud for processing physiological data. We developed a prototype of Fog-based unsupervised machine learning big data analysis for discovering patterns in physiological data. We...
Persistent Identifiers
Subjects
free text keywords: Computer Science - Computers and Society, Analytics, business.industry, business, Cloud computing, Wearable computer, Big data, Telecare, Human–computer interaction, Unsupervised learning, Computer science, Smartwatch, Edge computing
Related Organizations
16 references, page 1 of 2

[5] R. Patel, K. C. Hustad, K. P. Connaghan, and W. Furr, “Relationship between prosody and intelligibility in children with dysarthria,” Journal of medical speechlanguage pathology, vol. 20, no. 4, 2012.

[6] C. R. Watts, “A retrospective study of long-term treatment outcomes for reduced vocal intensity in hypokinetic dysarthria,” BMC Ear, Nose and Throat Disorders, vol. 16, no. 1, pp. 2, 2016.

[7] H. Dubey, N. Constant, A. Monteiro, M. Abtahi, D. Borthakur, L. Mahler, Y. Sun, Q. Yang, and K. Mankodiya, “Fog computing in medical internet-ofthings: Architecture, implementation, and applications,” in Handbook of Large-Scale Distributed Computing in Smart Healthcare. 2017, Springer International Publishing AG. [OpenAIRE]

[8] H. Dubey, R. Kumaresan, and K. Mankodiya, “Harmonic sum-based method for heart rate estimation using ppg signals affected with motion artifacts,” Journal of Ambient Intelligence and Humanized Computing, pp. 1-14, 2016.

[9] H. Dubey, M. R. Mehl, and K. Mankodiya, “Bigear: Inferring the ambient and emotional correlates from smartphone-based acoustic big data,” in EEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), Washington, DC, 2016, pp. 78-83., 2016, number doi: 10.1109/CHASE.2016.46. [OpenAIRE]

[10] H. Dubey, J. C. Goldberg, K. Mankodiya, and L. Mahler, “A multi-smartwatch system for assessing speech characteristics of people with dysarthria in group settings,” in IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom),, 2015. [OpenAIRE]

[11] A. Monteiro, H. Dubey, L. Mahler, Q. Yang, and K. Mankodiya, “Fit: A fog computing device for speech tele-treatments,” in IEEE Smart Computing (SMARTCOMP), 2016. [OpenAIRE]

[12] S. Hiremath and K. Yang, G.and Mankodiya, “Wearable internet of things: Concept, architectural components and promises for person-centered healthcare,” in Mobihealth Conference. IEEE, 2014. [OpenAIRE]

[13] H. Dubey, J. Yang, N. Constant, A. M. Amiri, Q. Yang, and K. Makodiya, “Fog data: Enhancing telehealth big data through fog computing,” in Fifth ASE BigData 2015, Kaohsiung, Taiwan. ACM.

[14] J. Andreu-Perez, C. C. Y. Poon, R. D. Merrifield, S. T.C. Wong, and G. Yang, “Big data for health,” IEEE journal of biomedical and health informatics, vol. 19, no. 4, pp. 1193-1208, 2015.

[15] R. K. Barik, H. Dubey, C. Misra, D. Borthakur, N. Constant, S. A. Sasane, R. K. Lenka, B. S. P. Mishra, H. Das, and K. Mankodiya, “Fog assisted cloud computing in era of big data and internet-of-things: Systems, architectures and applications,” in Cloud Computing for Optimization: Foundations, Applications, Challenges, p. 23. Springer, 2018. [OpenAIRE]

[16] J. Cancela, M. Pastorino, M. T. Arredondo, and O. Hurtado, “A telehealth system for parkinson's disease remote monitoring. the perform approach,” in 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),, 2013, pp. 7492-7495. [OpenAIRE]

[17] N. Constant, D. Borthakur, M. Abtahi, H. Dubey, and K. Mankodiya, “Fog-assisted wiot: A smart fog gateway for end-to-end analytics in wearable internet of things,” in The 23rd IEEE Symposium on High Performance Computer Architecture HPCA 2017,Austin, Texas, USA, 2017. [OpenAIRE]

[18] R. Barik, H. Dubey, R. K. Lenka, K. Mankodiya, T. Pratik, and S. Sharma, “Mistgis: Optimizing geospatial data analysis using mist computing,” in International Conference on Computing Analytics and Networking (ICCAN 2017). Springer, 2017.

[19] R. K. Barik, H. Dubey, R. K. Lenka, N.V.R. Simha, S. A. Sasane, C. Misra, and K. Mankodiya, “Fog computingbased enhanced geohealth big data analysis,” in 2017 International Conference on Intelligent Computing and Control (I2C2). IEEE, 2017.

16 references, page 1 of 2
Abstract
The increasing use of wearables in smart telehealth generates heterogeneous medical big data. Cloud and fog services process these data for assisting clinical procedures. IoT based ehealthcare have greatly benefited from efficient data processing. This paper proposed and evaluated use of low resource machine learning on Fog devices kept close to the wearables for smart healthcare. In state of the art telecare systems, the signal processing and machine learning modules are deployed in the cloud for processing physiological data. We developed a prototype of Fog-based unsupervised machine learning big data analysis for discovering patterns in physiological data. We...
Persistent Identifiers
Subjects
free text keywords: Computer Science - Computers and Society, Analytics, business.industry, business, Cloud computing, Wearable computer, Big data, Telecare, Human–computer interaction, Unsupervised learning, Computer science, Smartwatch, Edge computing
Related Organizations
16 references, page 1 of 2

[5] R. Patel, K. C. Hustad, K. P. Connaghan, and W. Furr, “Relationship between prosody and intelligibility in children with dysarthria,” Journal of medical speechlanguage pathology, vol. 20, no. 4, 2012.

[6] C. R. Watts, “A retrospective study of long-term treatment outcomes for reduced vocal intensity in hypokinetic dysarthria,” BMC Ear, Nose and Throat Disorders, vol. 16, no. 1, pp. 2, 2016.

[7] H. Dubey, N. Constant, A. Monteiro, M. Abtahi, D. Borthakur, L. Mahler, Y. Sun, Q. Yang, and K. Mankodiya, “Fog computing in medical internet-ofthings: Architecture, implementation, and applications,” in Handbook of Large-Scale Distributed Computing in Smart Healthcare. 2017, Springer International Publishing AG. [OpenAIRE]

[8] H. Dubey, R. Kumaresan, and K. Mankodiya, “Harmonic sum-based method for heart rate estimation using ppg signals affected with motion artifacts,” Journal of Ambient Intelligence and Humanized Computing, pp. 1-14, 2016.

[9] H. Dubey, M. R. Mehl, and K. Mankodiya, “Bigear: Inferring the ambient and emotional correlates from smartphone-based acoustic big data,” in EEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), Washington, DC, 2016, pp. 78-83., 2016, number doi: 10.1109/CHASE.2016.46. [OpenAIRE]

[10] H. Dubey, J. C. Goldberg, K. Mankodiya, and L. Mahler, “A multi-smartwatch system for assessing speech characteristics of people with dysarthria in group settings,” in IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom),, 2015. [OpenAIRE]

[11] A. Monteiro, H. Dubey, L. Mahler, Q. Yang, and K. Mankodiya, “Fit: A fog computing device for speech tele-treatments,” in IEEE Smart Computing (SMARTCOMP), 2016. [OpenAIRE]

[12] S. Hiremath and K. Yang, G.and Mankodiya, “Wearable internet of things: Concept, architectural components and promises for person-centered healthcare,” in Mobihealth Conference. IEEE, 2014. [OpenAIRE]

[13] H. Dubey, J. Yang, N. Constant, A. M. Amiri, Q. Yang, and K. Makodiya, “Fog data: Enhancing telehealth big data through fog computing,” in Fifth ASE BigData 2015, Kaohsiung, Taiwan. ACM.

[14] J. Andreu-Perez, C. C. Y. Poon, R. D. Merrifield, S. T.C. Wong, and G. Yang, “Big data for health,” IEEE journal of biomedical and health informatics, vol. 19, no. 4, pp. 1193-1208, 2015.

[15] R. K. Barik, H. Dubey, C. Misra, D. Borthakur, N. Constant, S. A. Sasane, R. K. Lenka, B. S. P. Mishra, H. Das, and K. Mankodiya, “Fog assisted cloud computing in era of big data and internet-of-things: Systems, architectures and applications,” in Cloud Computing for Optimization: Foundations, Applications, Challenges, p. 23. Springer, 2018. [OpenAIRE]

[16] J. Cancela, M. Pastorino, M. T. Arredondo, and O. Hurtado, “A telehealth system for parkinson's disease remote monitoring. the perform approach,” in 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),, 2013, pp. 7492-7495. [OpenAIRE]

[17] N. Constant, D. Borthakur, M. Abtahi, H. Dubey, and K. Mankodiya, “Fog-assisted wiot: A smart fog gateway for end-to-end analytics in wearable internet of things,” in The 23rd IEEE Symposium on High Performance Computer Architecture HPCA 2017,Austin, Texas, USA, 2017. [OpenAIRE]

[18] R. Barik, H. Dubey, R. K. Lenka, K. Mankodiya, T. Pratik, and S. Sharma, “Mistgis: Optimizing geospatial data analysis using mist computing,” in International Conference on Computing Analytics and Networking (ICCAN 2017). Springer, 2017.

[19] R. K. Barik, H. Dubey, R. K. Lenka, N.V.R. Simha, S. A. Sasane, C. Misra, and K. Mankodiya, “Fog computingbased enhanced geohealth big data analysis,” in 2017 International Conference on Intelligent Computing and Control (I2C2). IEEE, 2017.

16 references, page 1 of 2
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