demonstration

Demo: Exploiting IMU Sensors for IoT Enabled Health Monitoring

Authors:

Vivek Chandel,

Arijit Sinharay,

Nasimuddin Ahmed,

Avik GhoseAuthors Info & Claims

MobiSys '16 Companion: Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services Companion

Page 97

https://doi.org/10.1145/2938559.2948648

Published: 25 June 2016 Publication History

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Abstract

Inertial Measurement Units (IMUs) embedded in commercial mobile devices are a good choice for continuous monitoring in healthcare domain due to their attractive form factor and low power consumption. We present improved and accurate sensing algorithms to sense basic events like step count, stride length, fall, and calorie, with accuracies better than those of existing arts. The events can be directed to a server running event analytics, yielding important cues about subject's health. One of the important spheres is elderly health- care where such a system might promote a better insight to the subject's health parameters.

Fall Detection. For an improved fall detection, we propose the algorithm as in Figure 1, which shows a substantial improvement on mobifall dataset [3] compared to existing arts, with an average sensitivity of 0:855 and a more robust false event rejections. The detection can be used to trigger an automatic alarm for the need of an urgent attention to the elderly subject.

References

[1]

B. E. Ainsworth, W. L. Haskell, M. C. Whitt, M. L. Irwin, A. M. Swartz, S. J. Strath, W. L. O Brien, D. R. Bassett, K. H. Schmitz, P. O. Emplaincourt, et al. Compendium of physical activities: an update of activity codes and met intensities. Medicine and science in sports and exercise, 32(9; SUPP/1):S498--S504, 2000.

Google Scholar

[2]

V. Chandel, A. Duttachoudhury, A. Ghose, and C. Bhaumik. Actrak - unobtrusive activity detection and step counting using smartphones. In Proceedings of the Tenth International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, Mobiquitous '13. ACM, 2013.

Google Scholar

[3]

G. Vavoulas, M. Pediaditis, E. G. Spanakis, and M. Tsiknakis. The mobifall dataset: An initial evaluation of fall detection algorithms using smartphones. In Bioinformatics and Bioengineering (BIBE), 2013 IEEE 13th International Conference on, pages 1--4, Nov 2013.

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Chandel VJaiswal DGhose A(2017)InLocW: A reliable indoor tracking and guiding system for smartwatches with path re-routing2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)10.1109/PERCOMW.2017.7917648(697-702)Online publication date: Mar-2017
https://doi.org/10.1109/PERCOMW.2017.7917648

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Demo: Exploiting IMU Sensors for IoT Enabled Health Monitoring

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Published In

MobiSys '16 Companion: Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services Companion

June 2016

172 pages

ISBN:9781450344166

DOI:10.1145/2938559

General Chairs:
Rajesh Balan
Singapore Management University
,
Archan Misra
Singapore Management University
,
Program Chairs:
Landon Cox
Duke University
,
Yutaka Arakawa
NAIST
,
Xia Zhou
Dartmouth College
,
Robert LiKamWa
Rice University

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGOPS: ACM Special Interest Group on Operating Systems

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Association for Computing Machinery

New York, NY, United States

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Published: 25 June 2016

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SIGMOBILE

MobiSys'16: The 14th Annual International Conference on Mobile Systems, Applications, and Services

June 25 - 30, 2016

Singapore, Singapore

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Chandel VJaiswal DGhose A(2017)InLocW: A reliable indoor tracking and guiding system for smartwatches with path re-routing2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)10.1109/PERCOMW.2017.7917648(697-702)Online publication date: Mar-2017
https://doi.org/10.1109/PERCOMW.2017.7917648

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Exploiting IMU Sensors for IOT Enabled Health Monitoring

Detecting the Pre-impact of Falls in the Elderly, Along with the Use of an Airbag Belt for Protection Against Femoral Neck Fractures

Using Type-2 Fuzzy Models to Detect Fall Incidents and Abnormal Gaits among Elderly

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