http://ase.iha.dk/ Default.aspx?ID=9944 Quantitative Quantitative Comparison of the Comparison of the Accuracy Between the Accuracy Between the OLAM Continuous- OLAM Continuous- Tracking Device and Tracking Device and Commercial Commercial Monitoring Monitoring Shannon Cahill-Weisser Shannon Cahill-Weisser Mentor: Dr. Patrick Chiang Mentor: Dr. Patrick Chiang Department of Electrical Engineering Department of Electrical Engineering and Computer Science and Computer Science Oregon State University Oregon State University
Quantitative Comparison of the Accuracy Between the OLAM Continuous-Tracking Device and Commercial Monitoring. Shannon Cahill-Weisser Mentor: Dr. Patrick Chiang Department of Electrical Engineering and Computer Science Oregon State University. http://ase.iha.dk/Default.aspx?ID=9944. - PowerPoint PPT Presentation
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http://ase.iha.dk/Default.aspx?ID=9944
Quantitative Comparison Quantitative Comparison of the Accuracy Between of the Accuracy Between the OLAM Continuous-the OLAM Continuous-
Tracking Device and Tracking Device and Commercial MonitoringCommercial Monitoring
Shannon Cahill-WeisserShannon Cahill-WeisserMentor: Dr. Patrick ChiangMentor: Dr. Patrick Chiang
Department of Electrical Engineering Department of Electrical Engineering and Computer Scienceand Computer Science
Oregon State UniversityOregon State University
Why Make Vital Signs Monitors Wearable?
One third of physicians make decisions with incomplete information. [1]
In General...
• Assists diagnosis/prognosis
• Can indicate specific events
• Promotes patient independence [2]
[1] PricewaterhouseCoopers’ Health Research Institute, 2011[2] Hayes, et al., 2008
Based on PricewaterhouseCoopers Health Research Institute Physician Survey, 2010
Why Make Vital Signs Monitors Wearable?
Specific Examples...Activity:
• Energy expenditure [1]
• Gait velocity to predict
cognitive impairment [2]
Electrocardiogram: 2006: 36.3% of Americans have heart disease [3]
1. Duty-cycled activity data agrees highly with commercial data on an hourly scale.
2. Heart data is more sensitive to duty-cycle length.
3. Further post-processing is necessary to obtain accurate heart-rate data.
References
“Healthcare Unwired: New Business Models Delivering Care Anywhere” [Online], PricewaterhouseCoopers’ Health Research Institute, 2010, Available at: http://www.lindsayresnick.com/Resource_Links/Healthcare_Unwired.pdf, Accessed Sept 12, 2011.
T. Buracchio, H.H. Dodge, D. Howieson, D. Wasserman, and J. Kaye, "The Trajectory of Gait Speed Preceding Mild Cognitive Impairment", Arch Neurol., 2010; 67(8):980-986.
T. Hayes, M. Pavel, and J. Kaye, "An Approach for Deriving Continuous Health Assessment Indicators from In-Home Sensor Data" in Selected Papers from the 2007 International Conference on Technology and Aging , IOS Press, Amsterdam, Netherlands, 2008.
US Census Bureau, State & County Quickfacts [Online], Available from: (http://quickfacts.census.gov/qfd/states/00000.html , Accessed: Feb. 24, 2011.
American Heart Association, American Heart Disease and Stroke Statistics―2009 Update At-A-Glance (http://www.americanheart.org/presenter.jhtml?identifier=3037327), Accessed Feb. 24, 2011.
R.K. Albright, B.J. Goska, T.M. Hagen, M.Y. Chi, G. Cauwenberghs, and P. Y. Chiang, “OLAM: A Wearable, Non-Contact Sensor for Continuous Heart-Rate and Activity Monitoring,” accepted, IEEE Engineering in Medicine and Biology Conference, 2011.
K.Y. Chen, and D.R. Bassett, Jr., “The Technology of Accelerometry-Based Activity Monitors: Current and Future,” Medicine & Science in Sports & Exercise, American College of Sports Medicine, Indianapolis, IN, pp. S490-S500, 2005.
Bonomi, A. G. Bonomi and K. R. , “Advances in physical activity monitoring and lifestyle interventions in obesity: a review.”, International Journal of Obesity, 1-11, 2011.
MORE UPON REQUEST
Acknowledgements
• HHMI and URISC
• Dr. Patrick Chiang
• Dr. Stewart Trost
• Ben Goska, Ryan Albright, Samuel House, Sean Connell, Daniel Austin, and Robert Pawlowski