Quantitative Comparison
of the Accuracy Between
the OLAM ContinuousTracking 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
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
[1] PricewaterhouseCoopers’ Health Research Institute, 2011
[2] Hayes, et al., 2008
[2]
What Doctors Want in Monitoring
Digestive Health
Acid Reflux/Indigestion
Bladder Control
Cardiac Rhythms
Sleep Patterns
Pain Level
Caloric Intake
Exercise/Physical Activity
Vital Signs
Blood Sugar
Weight
0
10
20
30
40
Percent of Doctors Surveyed
Based on PricewaterhouseCoopers Health Research Institute Physician Survey, 2010
50
60
70
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
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
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.
Considerations for Any
Wearable Monitor

Biocompatibility

Durability

Efficiency

Data Quality


Signal to noise ratio
Particularly motion induced artefact
Considerations for the
OLAM
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
[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/
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
Counting Method
SAMPLING
SAMPLING
sleep
sleep
5 sec
54.5 sec
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.
Albright, Goska, Hagen, Chi, Cauwenberghs, and Chiang EMBS Conference, 2011
Count Sum (gs)
Comparison Between OLAM and
ActiGraph GT3X+
OLAM
80
60
GT3X-plus
40
OLAM Filtered
20
0
1
2
3
4
5
GT3X-plus
Filtered
"Hours" (set of 66 minute-count sums)
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
1
11
21
31
41
51
61
71
81
91
101
111
121
131
141
151
161
171
181
191
201
211
221
231
241
251
261
271
281
291
301
311
321
331
341
351
361
371
381
391
401
411
421
Counts (g's)
Comparison of "Minute" Scale Counts
0.5
0.45
0.4
0.35
Olam
Filtered
0.3
0.25
GT3X+
Filtered
0.2
0.15
0.1
0.05
0
Epoch Number
Stationary
Walking
on bench
Working at Computer
Heart Rate Data
Taped to Skin
In Belt Over Shirt
Heart Data
OLAM
Pulse Ox.
Absolute Difference
72
77
5.0
72
77
5.0
72
82
10.
72
88
16.
96
87
9.0
[1]
•
Compared to Crucial Medical Systems pulse oximeter
•
Avg. Difference: 9.0 bpm, Stdev: 4.5 bpm
•
Indicates higher sensitivity to cycling
[1] http://www.crucialmedicalsystems.com/oled-cms50c-fingertip-pulse-oximeter-andoxygen-meter-p-220.html
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.
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/wpcontent/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
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