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& how it enables impactful
health outcomes
© 2019 Valencell, Inc
Dr. Steven LeBoeuf
Oct 23rd, 2019
3. ©2019 Valencell. Incwww.valencell.com/patents
Definition of “data science”
The study of modeling physical
phenomena via data, using advanced
data analysis methods (i.e., statistics,
machine learning, and related methods).
Definition interpreted from: Hayashi C. (1998) What is Data Science ? Fundamental Concepts and a Heuristic Example. In: Hayashi C., Yajima K., Bock HH., Ohsumi
N., Tanaka Y., Baba Y. (eds) Data Science, Classification, and Related Methods. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Tokyo
4. ©2019 Valencell. Incwww.valencell.com/patents
Three key applications of data science
in biometric wearables
1) Free-living Clinical R&D: Enables new monitoring methods
& therapies via free-living, longitudinal studies
2) Personalized Direction: Enables personalized health
direction seamlessly, autonomously, and dynamically
3) Accurate & Convenient Multiparameter Monitoring:
Addresses the problem of accurately generating a plurality of
important biometrics and assessments in using a single, low-
power, wearable device
5. ©2019 Valencell. Incwww.valencell.com/patents
The big dream of biometric wearables –
improving health outcomes with personalized direction
Environmental
Context
Activity Context
Diet & caloric intake
Biometrics
What foods and activities are
making me more/less healthy?
What environments cause me
the most stress?
Am I becoming hypertensive
or prediabetic?
Am I exercising too much
or too little?
Am I about to have migraine, asthma
attack, spike in glucose, etc.?
Am I getting the right quality of sleep?
Time-of-Day
Cloud Inputs
Personalized Direction
How does my diet affect
my blood pressure?
Am I at risk of a cardiovascular event?
Am I over/under dosing on my medication?
6. ©2019 Valencell. Incwww.valencell.com/patents
The big dream of wearable biometric sensors –
being invisible & intangible
Ideally, wearable sensors are
completely seamless with
everyday living, and one single
sensor can measure everything
that’s important.
7. ©2019 Valencell. Incwww.valencell.com/patents
Valencell’s approach applies data science to
every aspect of the wearable solution
Improving
sensor
optomechanics
Reducing artifacts
caused by motion &
environmental noise
Generating new
biometrics &
improving old ones
Demonstrating
robust physiological
assessments
Demonstrating
consistent/reliable
user experiences
8. ©2019 Valencell. Incwww.valencell.com/patents
Valencell applies machine learning to wearable R&D
in developing both biometrics & personalized assessments
Machine Learning Biometrics Model
PPG Data Labels
Biometrics
Machine Learning Assessments Model
Context Labels
Personalized
Assessments
Additional
Biometrics
Confidence
Level
9. ©2019 Valencell. Incwww.valencell.com/patents
Our approach required Valencell to create new tools
1) PPG Analyzer: Enables the broader engineering team (who
aren’t all data scientists) to visually analyze both PPG data and
key transforms of PPG data
2) Cloud-Based PPG Data Collector: Collects PPG data (and
associated contextual data) from multiple body locations
simultaneously – in the field – and uploads raw PPG data to our
cloud for analysis
3) Database Reporting Tool: Enables real-time visualization and
reporting of up-to-date data collected in the field
16. ©2019 Valencell. Incwww.valencell.com/patents
Valencell’s goal – create an accurate alternative to the BP cuff
• Calibration-free, reflection-mode PPG-based cuff-less BP monitoring technology
• Must NOT require calibration of any kind; only 3 input parameters are required:
a PPG signal, a motion signal, & static biometrics (height, weight, age, and gender)
• Must enable in-session BP measurements with the subject at rest (as with a BP cuff),
with cuff-like tracking ranging from very low to very high BP values
• Must be substantially more convenient to use on a daily basis than a BP cuff
• Must be compatible with integration into a wearable device (an embedded solution)
• Is NOT for use with dosing of a medication, surgery, or medical emergencies
17. ©2019 Valencell. Incwww.valencell.com/patents
The PPG waveform contains a great deal of information,
but sometimes you have to dig for it…
Peak Amplitude
(Pulse Pressure)
RRi
(HRV, Cardiac Functioning)
Breathing Rate
(Metabolic Status)
Perfusion Variation
Heart Rate
Ideal PPG Waveform
An accurate, PPG-based
estimation of blood pressure,
cardiac output, & other
advanced metrics requires a
machine learning approach
18. ©2019 Valencell. Incwww.valencell.com/patents
PPG signals comprise blood pressure information,
but the information can not be derived accurately using linear methods
• Motion-Tolerant HR
• Motion-Tolerant RRi
• Motion-Tolerant PPG Magnitude
• Non-Pulsatile Magnitude
• Signal Quality
Valencell generates numerous motion-
tolerant PPG parameters each second,
including the PPG magnitude, which
correlates with blood pulse volume &
systolic blood pressure
The motion tolerant PPG magnitude
(pulse volume) tracks with increasing
systolic blood pressure
But the nonlinearities of this
relationship demand a machine
learning approach
Time (seconds)
SystolicBloodPressure(mmHg)
NormalizedPPGMagnitude(a.u.)
90-sec average
trend line of
Valencell’s PPG
Magnitude
19. ©2019 Valencell. Incwww.valencell.com/patents
Valencell’s approach comprises machine learning
applied to >10,000 of datasets from >5000 subjects
1) Massive data collection
Thousands of training datasets collected in
the US, Vietnam, & Philippines
2) BP model development
PPG-based model development
utilizing machine learning tools
3) Proving generalization
Model applied to test dataset
collected via ISO protocol
4) Embedded solution
Integrating model into Valencell’s
PerformTek® biometric operating system
20. ©2019 Valencell. Incwww.valencell.com/patents
Preliminary findings...
• Preliminary data collection shows cuff-like tracking of BP over a full range of BP values
• These results are particularly impressive, as the ISO test dataset was never used to
train the BP model – the ultimate generalization test
• The final report will be generated in Dec 2019 & the results are being submitted for
publication in a peer-reviewed journal in Q1 2020
21. ©2019 Valencell. Incwww.valencell.com/patents
Other examples of assessments created or in development using
Valencell sensor parameters & machine learning
• Glucose trending
• Enuresis
• Atrial fibrillation
• Hypertension assessment
• Cognitive load
• Objective pain measure
• Fall prediction
• Hydration assessment
• Lactate level
• Dosing requirements
• Fatigue
• Stress assessment
• Heart attack risk
• Stroke risk