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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. - PowerPoint PPT PresentationTRANSCRIPT
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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
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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
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Based on PricewaterhouseCoopers Health Research Institute Physician Survey, 2010
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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
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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
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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.
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Considerations for Any Wearable Monitor
Biocompatibility
Durability
Efficiency
Data Quality Signal to noise ratio Particularly motion induced artefactParticularly motion induced artefact
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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/
OLAM [1] GT3X+ [2] ActiTrainer [3]
Sensor Variety ECG, accelerometer, gyroscope
Accelerometer,light sensor
ECG accessory, accelerometer, light sensor
Battery Life 15 days min. 31 days 7-14 days
Memory 2 GB 512 MB 4 MB
Rate/Sensitivity 100 Hz/ ±2-8 g 30-100 Hz/ ±6g 30 Hz/ ±3g
Mounting Over or under Chest belt Polar heart strap
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Capacitive ECGSensor
Capacitive ECGSensor
3-D ADXL345 MEMS
Accelerometer
3-D ADXL345 MEMS
Accelerometer
100 Hz Sampling,5 sec per
minute
100 Hz Sampling,5 sec per
minute
MATLABMATLAB2.5 Hz Low Pass Filter0.25 Hz High Pass Filter2.5 Hz Low Pass Filter
0.25 Hz High Pass Filter
Obtain and compare counts over minutes and hours
Obtain and compare counts over minutes and hours
Sampling and Analysis Block Diagram
Albright, Goska, Hagen, Chi, Cauwenberghs, and Chiang EMBS Conference, 2011
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Counting Method
1. Average accelerometer magnitudes over number of samples. These are “counts”.
2. Add counts for desired time period.
3. Analysis code written to “window” continuous GT3X+ data.
SAMPLINGSAMPLING SAMPLINGSAMPLING
sleepsleepsleepsleep
5 sec
54.5 sec
Albright, Goska, Hagen, Chi, Cauwenberghs, and Chiang EMBS Conference, 2011
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Hour Counts Minute Counts
μ Difference (%), Unfiltered 6.92 7.84
σ Difference (%), Unfiltered 4.00 3.52
μ Difference (%), Filtered 5.67 315.79
σ Difference (%) Filtered 5.05 1072.05
• Agreement good in unfiltered and hourly data• Error high in filtered minute data• Sources: reaction time, window matching, extrapolation
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on bench on bench
Stationary
Walking
Working at Computer
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Heart Rate Data
Taped to Skin In Belt Over Shirt
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Heart Data
• Compared to Crucial Medical Systems pulse oximeter
• Avg. Difference: 9.0 bpm, Stdev: 4.5 bpm
• Indicates higher sensitivity to cycling
OLAM Pulse Ox. Absolute Difference
72 77 5.0
72 77 5.0
72 82 10.
72 88 16.
96 87 9.0
[1] http://www.crucialmedicalsystems.com/oled-cms50c-fingertip-pulse-oximeter-and-oxygen-meter-p-220.html
[1]
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Conclusions
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.
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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
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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