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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.

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  1. 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

  2. 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

  3. Based on PricewaterhouseCoopers Health Research Institute Physician Survey, 2010

  4. 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] Contextual vs. clinical measurement [1] Chen et. al., 2005 [2] Buracchio et. al., 2010 [3] CDC, 2009

  5. Linus Pauling Institute Collaboration • OLAM worn to study effects of micronutrient • Worn by 10 subjects in an 6 week trial • Study conducted with lab of Dr. Tory Hagen Albright, Goska, Hagen, Chi, Cauwenberghs, and Chiang EMBS Conference, 2011

  6. Project Plan Objective: Evaluate performance of ECG against pulse oximeter Compare activity data to commercial monitor data Apply analysis to LPI study data Hypothesis: Activity data will be comparable to commercial sensing. ECG data will contain motion artifact.

  7. Considerations for Any Wearable Monitor Biocompatibility Durability Efficiency Data Quality Signal to noise ratio Particularly motion induced artefact

  8. Considerations for the OLAM [1] Albright, Goska, Hagen, Chi, Cauwenberghs, and Chiang EMBS Conference, 2011 [2] http://www.theactigraph.com/products/gt3x-plus/ [3] http://www.theactigraph.com/products/actitrainer/

  9. Sampling and Analysis Block Diagram 3-D ADXL345 MEMS Accelerometer Capacitive ECG Sensor 100 Hz Sampling, 5 sec per minute MATLAB 2.5 Hz Low Pass Filter 0.25 Hz High Pass Filter Obtain and compare counts over minutes and hours Albright, Goska, Hagen, Chi, Cauwenberghs, and Chiang EMBS Conference, 2011

  10. Counting Method • Average accelerometer magnitudes over number of samples. These are “counts”. • Add counts for desired time period. • Analysis code written to “window” continuous GT3X+ data. SAMPLING SAMPLING sleep sleep 5 sec 54.5 sec Albright, Goska, Hagen, Chi, Cauwenberghs, and Chiang EMBS Conference, 2011

  11. Agreement good in unfiltered and hourly data • Error high in filtered minute data • Sources: reaction time, window matching, extrapolation

  12. Stationary Walking on bench on bench Working at Computer

  13. Heart Rate Data Taped to Skin In Belt Over Shirt

  14. Heart Data • Compared to Crucial Medical Systems pulse oximeter • Avg. Difference: 9.0 bpm, Stdev: 4.5 bpm • Indicates higher sensitivity to cycling [1] [1] http://www.crucialmedicalsystems.com/oled-cms50c-fingertip-pulse-oximeter-and-oxygen-meter-p-220.html

  15. Conclusions • Duty-cycled activity data agrees highly with commercial data on an hourly scale. • Heart data is more sensitive to duty-cycle length. • Further post-processing is necessary to obtain accurate heart-rate data.

  16. 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. • ActiGraph, ActiTrainer Activity Monitor [Online], Available at: http://www.theactigraph.com/products/actitrainer/. Accessed: Sept 12, 2011. • ActiGraph, ActiGraph GT3X+ Monitor [Online], Available at: www.theactigraph.com/wp-content/uploads/ActiGraphCT3X+Specs.pdf, Accessed: Sept, 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

  17. Acknowledgements • HHMI and URISC • Dr. Patrick Chiang • Dr. Stewart Trost • Ben Goska, Ryan Albright, Samuel House, Sean Connell, Daniel Austin, and Robert Pawlowski • The lab of Dr. Tory Hagen

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