research-article

eButton: A Wearable Computer for Health Monitoring and Personal Assistance

Authors:

Hsin-Chen Chen,

Wenyan JiaAuthors Info & Claims

DAC '14: Proceedings of the 51st Annual Design Automation Conference

Pages 1 - 6

https://doi.org/10.1145/2593069.2596678

Published: 01 June 2014 Publication History

Abstract

Recent advances in mobile devices have made profound changes in people's daily lives. In particular, the impact of easy access of information by the smartphone has been tremendous. However, the impact of mobile devices on healthcare has been limited. Diagnosis and treatment of diseases are still initiated by occurrences of symptoms, and technologies and devices that emphasize on disease prevention and early detection outside hospitals are under-developed. Besides healthcare, mobile devices have not yet been designed to fully benefit people with special needs, such as the elderly and those suffering from certain disabilities, such blindness. In this paper, an overview of our research on a new wearable computer called eButton is presented. The concepts of its design and electronic implementation are described. Several applications of the eButton are described, including evaluating diet and physical activity, studying sedentary behavior, assisting the blind and visually impaired people, and monitoring older adults suffering from dementia.

References

[1]

Centers for Disease Control and Prevention 2009. Chronic diseases: the power to prevent, the call to control, at-a-glance. http://www.cdc.gov/chronicdisease/resources/publications/aag/chronic.htm

[2]

Centers for Disease Control and Prevention 2013 Rising Health Care Costs are Unsustainable. http://www.cdc.gov/workplacehealthpromotion/businesscase/reasons/rising.html

[3]

Flegal, K.M., Carroll, M.D., Ogden, C.L., and Curtin, L.R. 2010. Prevalence and trends in obesity among US adults, 1999--2008. JAMA 303, 3 (Jan 20), 235--241.

[4]

U.S. Department of Health and Human Services. 2001. Overweight and obesity: a major public health issue. http://odphp.osophs.dhhs.gov/pubs/prevrpt/01fall/pr.htm

[5]

Hammond, R.A. and Levine, R. 2010. The economic impact of obesity in the United States. Diabetes Metab Syndr Obes 3, 285--295.

[6]

Sun, M., Fernstrom, J.D., Jia, W., Hackworth, S.A., Yao, N., Li, Y., Li, C., Fernstrom, M.H., and Sclabassi, R.J. 2010. A wearable electronic system for objective dietary assessment. J Am Diet Assoc 110, 1 (Jan), 45--47.

[7]

Bai, Y., Li, C., Yue, Y., Jia, W., Li, J., Mao, Z.-H., and Sun, M., 2012. Designing a wearable computer for lifestyle evaluation. In Proceedings of the 38th Annual Northeast Biomedical Engineering Conference (NEBEC) (Philadelphia, PA, March 16--18 2012), 243--244.

[8]

Nie, J., Wei, Z., Jia, W., Li, L., Fernstrom, J.D., Sclabassi, R.J., and Sun, M., 2010. Automatic detection of dining plates for image-based dietary evaluation. In Proceedings of the Conf Proc IEEE Eng Med Biol Soc (Buenos Aires, Argentina, August 31 - September 4 2010), 4312--4315.

[9]

Chen, H.C., Jia, W., Yue, Y., Li, Z., Sun, Y.N., Fernstrom, J.D., and Sun, M. 2013. Model-based measurement of food portion size for image-based dietary assessment using 3D/2D registration. Meas Sci Technol 24, 10 (Oct).

[10]

Jia, W., Chen, H.C., Yue, Y., Li, Z., Fernstrom, J., Bai, Y., Li, C., and Sun, M. 2013. Accuracy of food portion size estimation from digital pictures acquired by a chest-worn camera. Public Health Nutr(Dec 4), 1--11.

[11]

Jia, W., Yue, Y., Fernstrom, J.D., Zhang, Z., Yang, Y., and Sun, M., 2012. 3D localization of circular feature in 2D image and application to food volume estimation. In Proceedings of the Proc. IEEE 34th Annual Conf. on Engr. in Medicine and Biology (San Diego, CA, Aug.28 - Sept. 1 2012), 4545--4548.

[12]

Ahuja, J.K.A., Montville, J.B., Omolewa-Tomobi, G., Heendeniya, K.Y., Martin, C.L., Steinfeldt, L.C., Anand, J., Adler, M.E., Lacomb, R.P., and Moshfegh, A.J. 2012. USDA Food and Nutrient Database for Dietary Studies, 5.0. U.S. Department of Agriculture, Agricultural Research Service, Food Surveys Research Group, Beltsville, MD.

[13]

Kerr, J., Marshall, S.J., Godbole, S., Chen, J., Legge, A., Doherty, A.R., Kelly, P., Oliver, M., Badland, H.M., and Foster, C. 2013. Using the sensecam to improve classifications of sedentary behavior in free-living settings. American Journal of Preventive Medicine 44, 3 (Mar), 290--296.

[14]

ActiGraph Service 2011. What are counts? https://help.theactigraph.com/entries/20723176-what-are-counts

[15]

Ainsworth, B.E., Haskell, W.L., Herrmann, S.D., Meckes, N., Bassett, D.R., Jr., Tudor-Locke, C., Greer, J.L., Vezina, J., Whitt-Glover, M.C., and Leon, A.S. 2011. 2011 compendium of physical activities: a second update of codes and MET values. Med Sci Sports Exerc 43, 8 (Aug), 1575--1581.

[16]

Li, Z., Wei, Z., Jia, W., and Sun, M., 2013. Daily life event segmentation for lifestyle evaluation based on multi-sensor data recorded by a wearable device. In Proceedings of the 35th IEEE Int. Conf. Engineering in Medicine and Biology (Osaka, Japan, July 3-7 2013), 2858--2861.

[17]

Zhang, W., Jia, W., and M. Sun, 2010. Segmentation for efficient browsing of chronical video recorded by a wearable device. In Proceedings of the Proc. IEEE 36th Northeast Biomedical Engineering Conference (New York, NY, March 26-28 2010).

[18]

Li, Z., 2013. Study on analytic methods for human physical activity recognition based on wearable systems. Doctoral Thesis. Ocean University of China.

[19]

Zhang, H., Li, L., Jia, W., Fernstrom, J.D., Sclabassi, R.J., Mao, Z.H., and Sun, M. 2011. Physical activity recognition based on motion in images acquired by a wearable camera. Neurocomputing 74, 12--13 (Jun 1), 2184--2192.

Digital Library

[20]

Burges, C.J.C. 1998. A tutorial on Support Vector Machines for pattern recognition. Data Mining and Knowledge Discovery 2, 2 (Jun), 121--167.

Digital Library

[21]

Bai, Y., 2014. A Wearable Indoor Navigation System for the Blind and Visually Impaired Individuals. Doctoral Thesis. University of Pittsburgh.

[22]

Bai, Y., Jia, W., Zhang, H., Mao, Z.H., and Sun, M., 2013. Helping the blind to find the floor of destination in multistory buildings using a barometer. In Proceedings of the 35th IEEE Int. Conf. Engineering in Medicine and Biology (Osaka, Japan, July 3-7 2013), 4738--4741.

[23]

PandaCare: Demo Prepaing, 2013. https://blogs.cornell.edu/cornellcup2013pandacare/

[24]

Li, C., Bai, Y., Li, M., Lang, P., Sun, M., Chen, Y., and Sejdic, E. 2013. PandaCare: electronic unit for demensia care. Final Report, 2013 Cornnel Cup Competition.

[25]

STARLINO, 2011. DCM tutorial -- an introduction to orientation kinematics. http://www.starlino.com/dcm_tutorial.html

Cited By

Mamish JAlharbi RSen SHolla SKamath PSangar YAlshurafa NHester J(2024)NIR-sighted: A Programmable Streaming Architecture for Low-Energy Human-Centric Vision ApplicationsACM Transactions on Embedded Computing Systems10.1145/367207623:6(1-26)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3672076
Liang FSu ZSheng W(2024)Multimodal Monitoring of Activities of Daily Living for Elderly CareIEEE Sensors Journal10.1109/JSEN.2024.336618824:7(11459-11471)Online publication date: 1-Apr-2024
https://doi.org/10.1109/JSEN.2024.3366188
Huang YHassan MHe JHiggins JMcCrory MEicher-Miller HThomas JSazonov EZhu F(2024)Automatic Recognition of Food Ingestion Environment from the AIM-2 Wearable Sensor2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW63382.2024.00372(3685-3694)Online publication date: 17-Jun-2024
https://doi.org/10.1109/CVPRW63382.2024.00372
Show More Cited By

Index Terms

eButton: A Wearable Computer for Health Monitoring and Personal Assistance
1. Applied computing
  1. Life and medical sciences
2. Computer systems organization
  1. Embedded and cyber-physical systems
  2. Real-time systems

Recommendations

Designing a wellness self-management tool for older adults: results from a field trial of YourWellness
PervasiveHealth '14: Proceedings of the 8th International Conference on Pervasive Computing Technologies for Healthcare

It is recognized that empowering individuals to manage their own health and wellbeing will result in more cost-effective healthcare systems, improved health outcomes and will encourage healthy individuals to remain that way. With the advent of the ...
The DAPHNE Project

This paper summarizes the DAPHNE Project presented at the ICTH 2016. The DAPHNE (Data-as-a-Service Platform for Healthy lifestyle and preventive medicine) Project is supported by the European Community's FP7 research programme with the objective of ...
NFC as a Childhood Obesity Treatment Tool

Childhood Obesity is associated with a wide range of serious health complications and constitutes an increased risk of premature syndromes, including diabetes or heart diseases. Its treatment seems to be complicated. So, in order to help parents we have ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

DAC '14: Proceedings of the 51st Annual Design Automation Conference

June 2014

1249 pages

ISBN:9781450327305

DOI:10.1145/2593069

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

EDAC: Electronic Design Automation Consortium
SIGBED: ACM Special Interest Group on Embedded Systems
SIGDA: ACM Special Interest Group on Design Automation
IEEE-CEDA

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 June 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

DAC '14

DAC '14: The 51st Annual Design Automation Conference 2014

June 1 - 5, 2014

CA, San Francisco, USA

Acceptance Rates

Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

87
Total Citations
View Citations
1,272
Total Downloads

Downloads (Last 12 months)59
Downloads (Last 6 weeks)3

Reflects downloads up to 17 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Mamish JAlharbi RSen SHolla SKamath PSangar YAlshurafa NHester J(2024)NIR-sighted: A Programmable Streaming Architecture for Low-Energy Human-Centric Vision ApplicationsACM Transactions on Embedded Computing Systems10.1145/367207623:6(1-26)Online publication date: 11-Sep-2024
https://dl.acm.org/doi/10.1145/3672076
Liang FSu ZSheng W(2024)Multimodal Monitoring of Activities of Daily Living for Elderly CareIEEE Sensors Journal10.1109/JSEN.2024.336618824:7(11459-11471)Online publication date: 1-Apr-2024
https://doi.org/10.1109/JSEN.2024.3366188
Huang YHassan MHe JHiggins JMcCrory MEicher-Miller HThomas JSazonov EZhu F(2024)Automatic Recognition of Food Ingestion Environment from the AIM-2 Wearable Sensor2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW63382.2024.00372(3685-3694)Online publication date: 17-Jun-2024
https://doi.org/10.1109/CVPRW63382.2024.00372
Liang FSu ZZhang ZChen ZSheng W(2024)A Deep Q Learning Based Method for Optimized Monitoring of Activities of Daily Living U sing a Smart Watch and a Companion Robot2024 4th International Conference on Computer Communication and Artificial Intelligence (CCAI)10.1109/CCAI61966.2024.10603092(503-508)Online publication date: 24-May-2024
https://doi.org/10.1109/CCAI61966.2024.10603092
Lo FQiu JJobarteh MSun YWang ZJiang SBaranowski TAnderson AMcCrory MSazonov EJia WSun MSteiner-Asiedu MFrost GLo B(2024)AI-enabled wearable cameras for assisting dietary assessment in African populationsnpj Digital Medicine10.1038/s41746-024-01346-87:1Online publication date: 5-Dec-2024
https://doi.org/10.1038/s41746-024-01346-8
Chen XKamavuako E(2023)Vision-Based Methods for Food and Fluid Intake Monitoring: A Literature ReviewSensors10.3390/s2313613723:13(6137)Online publication date: 4-Jul-2023
https://doi.org/10.3390/s23136137
Nadeem MShen HChoy LBarakat J(2023)Smart Diet Diary: Real-Time Mobile Application for Food RecognitionApplied System Innovation10.3390/asi60200536:2(53)Online publication date: 20-Apr-2023
https://doi.org/10.3390/asi6020053
Namkhah ZFatemi SMansoori ANosratabadi SGhayour-Mobarhan MSobhani S(2023)Advancing sustainability in the food and nutrition system: a review of artificial intelligence applicationsFrontiers in Nutrition10.3389/fnut.2023.129524110Online publication date: 16-Nov-2023
https://doi.org/10.3389/fnut.2023.1295241
Kong NMoy FOng STahir GLoo C(2023)MyDietCam: Development and usability study of a food recognition integrated dietary monitoring smartphone applicationDIGITAL HEALTH10.1177/205520762211493209Online publication date: 9-Jan-2023
https://doi.org/10.1177/20552076221149320
Hosseini-Toudeshky HKersten-Oertel M(2023)Integrating Real-Time Health Status into Everyday Objects: A Design Case Study on Enhancing Diabetic Health Monitoring with Artistic CreationsProceedings of the 25th International Conference on Mobile Human-Computer Interaction10.1145/3565066.3608696(1-6)Online publication date: 26-Sep-2023
https://dl.acm.org/doi/10.1145/3565066.3608696
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents