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Affordable Sensing Based Healthcare

Data-Driven Screening, Diagnosis and Therapy

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Communication Systems and Networks (COMSNETS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 10340))

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Abstract

In order to move from the current “illness”-driven model to a “wellness”-driven model in healthcare, one needs to build affordable, easily usable and mass deployable solutions. This is particularly true for developing countries like India. In this talk we look at early detection and screening for lifestyle diseases like coronary artery disease (CAD) and diabetes using mobile phones and low-cost attachments to mobile phones followed by signal processing and machine learning based analytics. We also look at creating an affordable tele-home-care based rehabilitation therapy solution for stroke patients using Kinect to help in diagnosis, assessment and therapy compliance. We present results on pilot studies done on patients in India and also on open datasets.

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Notes

  1. 1.

    http://www.aoa.acl.gov/Aging_Statistics/index.aspx.

  2. 2.

    http://www.neuroathome.net/p/home.html.

  3. 3.

    http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3511929/.

  4. 4.

    https://www.vicon.com/file/vicon/gait-analysis-print-ready-19082016-web-52778.pdf.

  5. 5.

    https://www.atrainceu.com/course-all/coronary-artery-disease-cad-041.

  6. 6.

    http://www.ncbi.nlm.nih.gov/pubmed/7743682.

  7. 7.

    http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1741307/pdf/v075p00463.pdf.

  8. 8.

    http://www.idf.org/about-diabetes/facts-figures.

  9. 9.

    www.physionet.org.

References

  1. Dey, S., Biswas, S., Pal, A., Mukherjee, A., Garain, U., Mandana, K.: CAD patient classification using MIMIC-II. In: Giokas, K., Bokor, L., Hopfgartner, F. (eds.) eHealth 360°. LNICST, vol. 181, pp. 370–375. Springer, Cham (2017). doi:10.1007/978-3-319-49655-9_44

    Chapter  Google Scholar 

  2. Banerjee, R., et al.: Time-frequency analysis of phonocardiogram for classifying heart disease. In: Computing in Cardiology (CinC), September 2016

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  3. Ramu Reddy, V., et al.: PerDMCS: weighted fusion of heart rate variability features for robust and efficient diabetes mellitus classification. In: HealthInf, Portugal, February 2017

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  4. Sinha, A., et al.: Accurate upper body rehabilitation system using Kinect. In: EMBC, Orlando, USA, August 2016

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Acknowledgment

I thank all the scientists and researchers working in TCS Research and Innovation in Health Sensing projects – the results of their combined work is presented in this paper. I also thank our doctor consultants for their involvement and support for the medical domain knowledge.

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Authors and Affiliations

  1. TCS Research and Innovation, Tata Consultancy Services Ltd., Kolkata, India

    Arpan Pal

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  1. Arpan Pal
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Correspondence to Arpan Pal .

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Editors and Affiliations

  1. King’s College London, London, United Kingdom

    Nishanth Sastry

  2. IIT Kharagpur, Kharagpur, West Bengal, India

    Sandip Chakraborty

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Pal, A. (2017). Affordable Sensing Based Healthcare. In: Sastry, N., Chakraborty, S. (eds) Communication Systems and Networks. COMSNETS 2017. Lecture Notes in Computer Science(), vol 10340. Springer, Cham. https://doi.org/10.1007/978-3-319-67235-9_1

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  • DOI: https://doi.org/10.1007/978-3-319-67235-9_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67234-2

  • Online ISBN: 978-3-319-67235-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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