There are likely to be hundreds use cases for wearables across many different industries and markets that wearables will enable. The form factors, data, and user interfaces will likely be very different, but the key is to understand that the wearable devices are one part of a comprehensive user experience that supports achieving specific goals or objectives. Even the most common current use case - fitness and activity tracking - is being vastly underserved by the offerings in the market today because they lack a compelling user experience. And it’s critical we get those objectives and user experiences right if the wearables market is going to realize the massive growth that many are predicting in the next few years.

1. ©2015 Valencell, Inc. 1
What if there is no killer app for wearables?...
IEEE Rock Stars of Wearables
September 2015

2. ©2015 Valencell, Inc. 2
The Take-Away
There may be no single “killer app” that drives
mass consumer adoption of biometric wearables
of all kinds.
However, accurate biometric sensor data, in
context of validated physiological models, can be
applied towards a suite of continually compelling
use cases at scale.

3. ©2015 Valencell, Inc. 3
There is no “killer app” driving mass
consumer adoption of wearable trackers
Listening
to music
Word-
processing
Mobile
phone calls
Mobile email
& internet
Mobile media ?
Although wearable devices are becoming mainstream, consumer
adoption is still well below ~50M units world-wide.

4. ©2015 Valencell, Inc. 4
The big vision – seamless personalized
direction towards better performance,
fitness, & health
Environmental
Context
Activity Context
Diet & caloric intake
Biometrics
What foods are making me
more/less healthy?
What environments
cause me the most
stress?
What is my minimum viable
activity level (MVAL) for
good health?
Am I exercising too much
or too little?
What activities are making
me more/less fit?
How can I improve my
sleep?
Time-of-Day
Cloud Inputs
Personalized Direction

5. ©2015 Valencell, Inc. 5
Numerous barriers exist for a killer app
in wearable trackers
• Diversity of needs & styles – A diversity of personal needs, goals,
preferences, & “style” determine the desired use case & form-factor
• Sensor functionality limitations – Most wearable trackers cannot
accurately sense the right type of data, in a single consumer-friendly form-
factor, required for the most broadly appealing user experiences
• No sensor standards – No standards exist for biometric sensor
performance (in terms of accuracy, data cadence, functionality, etc); thus
health & fitness apps do not scale across HW brands
• Chemistry can be instantaneous, but biology takes time – Killer apps
based on biometric data require years of validation to prove the value
proposition
• Personalized value propositions – The most compelling consumer
applications have complex, personalized value props, making simplified
messaging & mass scalability difficult

6. ©2015 Valencell, Inc. 6
Lifestyle In-Session Personal Health
Three key use case classifications for
wearables, with many sub-classes
Lifestyle In-Session Health Monitoring
Wearability 24/7 comfort; visible Stable during target
activities; visible or invisible
24/7 or “regular
monitoring”; invisible
Accuracy “Good enough” for
assessments
Real-time accuracy critical Real-time accuracy critical
Battery Life ≥ 3 days ≥ 3 hours ≥ 1 month
Engagement Daily, weekly, & monthly Daily, weekly, & monthly Clinician-dependent

7. ©2015 Valencell, Inc. 7
Rich form-factor diversity in biometric
wearables using PPG
Armbands

8. ©2015 Valencell, Inc. 8
Total Steps
1130
Calories
210
Cadence
125
Heart Rate
135
You are in the
“fat burning zone”
Your cardiovascular
fitness has improved
by 10% this month.
Here’s what you’re
doing right.
Old School Wearables
Giving you numbers
Next-Gen Wearables
Providing you personalized direction
Next-generation user experiences (UX)
are moving from simply reporting numbers
to providing personalized direction

9. ©2015 Valencell, Inc. 9
Sensing
Three layers of UX in fitness wearables
• What activity am I doing?
• How many steps have I taken?
• What is my activity record?
Directing
• Am I exercising enough to
maintain my fitness level?
• What foods are best for me?
• What can I (uniquely) do to get
healthier?
Assessing
• How is my activity affecting
my fitness?
• Is my fitness improving?...
• Am I getting worse?...
Activity Tracking
or
Biometric Tracking
Activity Tracking
+
Biometric Tracking
Activity Tracking
+
Biometric Tracking
+
Models & Contextual Analysis
Increasing mass-consumer value & increasing sensor accuracy requirements

10. ©2015 Valencell, Inc. 10
Cadence
(Step
Rate)
Time
Beats(Steps)/MinAccurate heart rate + cadence is critical
for personalized fitness assessments
Accurate HRM Mood Ring HRM
HRV
HR
Response
Peak HR/
VO2max
HR
Recovery
Cardiac
Efficiency
A key problem is that wrist-worn biometric wearables haven’t been
able to demonstrate the accuracy needed for personalized direction

11. ©2015 Valencell, Inc. 11
Accurate fitness assessments can drive
new health applications in wearables
Assessment Definition What is means for fitness What is means for health
VO2max Aerobic capacity –
primary measure of
chronic change to
cardiovascular fitness
Higher VO2max is correlated
with better performance
during aerobic activities
Higher VO2max is correlated with
lower mortality & improved
recovery from a cardiac event
[Anderson, Jetté, Kodama, Lee]
Resting Heart
Rate (HRrest)
HR during an awake
period of no exertion
Decreasing Resting HR is
correlated with increasing
fitness
Steadily increasing Resting HR is
correlated with the progression of
cardiovascular disease
[Arnold, Fox, Nauman]
HR Recovery HR over 1-minute
after intense exercise
Higher HR Recovery implies
better exercise endurance
Higher HR Recovery implies
better cardiovascular health
[Ching, Cho, Lipinski, Nishime]
HR Response HR over 1-minute at
the start of exercise
Higher HR Response can
imply low cardiac readiness
for exercise
Higher HR Response paired with
“chronotropic incompetence” can
predict carotid atherosclerosis
[Falcone, Jaqoda, Jae, Maddox, Myers]
Cardiac
Efficiency
Average cadence
divided by average
heart rate (at steady
state): Cavg/HRavg
The higher cardiac efficiency,
the less heart beats are
needed for all physical
activities
Steadily declining cardiac
efficiency is correlated with the
onset of hypertension
[Laine, Sung]
HRV Heart rate variability --
statistical variability of
RR-intervals
HRV can diagnose
psychosocial stress &
overtraining in exercise
HRV can predict atrial fibrillation &
arrhythmia
[Hohnloser, McManus, Park, Valkama]

12. ©2015 Valencell, Inc. 12
Form-factor & user comfort are also
critical for mass consumer adoption
Fortunately, motion-
tolerant PPG sensor
technology offers the
ability to measure
virtually everything
that’s important in
one wearable device

13. ©2015 Valencell, Inc. 13
• Core technology gives consumer wearables the ability to continuously & accurately measure
weak blood flow (PPG) signals even during extreme physical activity
• State-of-the-art signal extraction is required: Optical noise from skin motion, body motion, &
sunlight must be actively removed from the PPG signals in real-time
• Highly accurate signal extraction enable more advanced biometrics: heart rate variability,
respiration rate, blood oxygen level, blood pressure, VO2, & more
• Motion sensor context + PPG enables advanced health & fitness assessments
Sunlight
Noise
Motion-tolerant optical heart rate sensing –
the basic concept

14. ©2015 Valencell, Inc. 14
Peak Amplitude
(Pulse Pressure)
RRi
(HRV, Cardiac Functioning)
Breathing Rate
(Metabolic Status)
Perfusion Variation
Motion-tolerant PPG is changing the
game in wearables, but standards are needed
Heart Rate
Blood
Pressure
Serial output standards would
help scale sensor technology
for use in multiple form-factors
& consumer applications Valencell US Patent 8,923,941

15. ©2015 Valencell, Inc. 15
Testing & validation is critical – for each
new form-factor & each new application
• Testing protocols that match the use cases - resting, lifestyle activities, mild
exercise, aggressive exercise, interval training, etc.
• Validation datasets on 30+ participants of multiple physical habitus, gender, &
skin tone
• Biometric analysis should include:
regression analysis (R2) & Bland-Altman
analysis
• Diagnosis analysis should include: true
positives, false positives, true negatives,
false negatives, & total positives &
negatives
• Ideally, there should be independent
validation of each metric

16. ©2015 Valencell, Inc. 16
Validating the user experience takes
time, but prevents bad outcomes like these...
“I’d been hitting my 10,000 steps each day & thought, OK, so now what?”
“I woke up feeling well-rested, but my tracker said I had bad sleep. I woke up
feeling tired, but my tracker said I had good sleep. After a while, I quit trusting it. ”
“Kept getting the same information each day... Nothing new, & so I lost interest.”
“It was an experiment for me anyways... I tried it, & I didn’t think it was worth the
money, so I returned it... Just didn’t interest me.”
“I saw how many calories I was burning, but how was that affecting my fitness? It
wasn’t clear if I was getting in better shape.”
“I thought it would help me lose weight, but it didn’t.”
“... Battery died, & I started using my cell phone [pedometer] instead.”
“Used it for 2 weeks... started walking... & gained weight!”

17. ©2015 Valencell, Inc. 17
Ajzen’s “Theory of Planned Behavior”
can be applied towards making better apps
“Used it for 2 weeks...
started walking... &
gained weight!”
“... It wasn’t clear if I
was getting in better
shape.”
“... Battery died, & I
started using my cell
phone [pedometer]
instead.”

18. ©2015 Valencell, Inc. 18
Wearable biometric sensor systems
should provide both acute & chronic
feedback for ultimate personalization
Local
Processing
Real-Time (Acute) Feedback
Sensor Data
Data for
Storage
Remote
Processing
Personalized
Processing
Updates
Long-term (Chronic) Feedback
Based on both personalized &
generalized physiological models

19. ©2015 Valencell, Inc. 19
Hypothesis: biometrics change over time
in a continually meaningful, interesting, &
controllable manner...
• 6 week study of heterogeneous participant mix with varying fitness levels, skin
tones, genders, & BMI
• Standard assessment of VO2max & lactate threshold using indirect calorimetry
• 2 days/week of high-intensity circuit training
• 3 days/week of cardiovascular training on treadmill
• Specific 15-min warm-up prior to each treadmill session – allowing for
assessment of fitness changes from week-to-week
• Benchmark sensors – CSHRM, indirect calorimetry, calibrated treadmill
• DUT – PerformTek-powered earbuds
Study Protocol:

20. ©2015 Valencell, Inc. 20
Accurately tracking biometrics + activity
can expose positive fitness outcomes
Resting HR
reduced by 10%
Cardiac Efficiency
increased by 10%
VO2max
didn’t change much
HR recovery
rose & fell

21. ©2015 Valencell, Inc. 21
Your cardiovascular
fitness has improved
by 10% this month.
Here’s what you’re
doing right.
You are overtraining.
Here’s how to improve
For mass-consumer audiences, simpler
user interfaces may be more easily adopted
You now Your month-1
target
Avg for your
demographic
Cardiac Efficiency
(higher is better)
Where you
started
Exercise
intensity
Exercise
Duration
Spacing out your workouts will help
optimize your training. Click here to
update your workout plan.

22. ©2015 Valencell, Inc. 22
Biometric feedback can be applied in
stages to make the user experience
continually interesting...
Focus:
Resting HR
Focus:
Cardiac Eff.
Focus:
VO2max & HR Recovery
Focus:
Performance Improvement/Weight-Loss

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Natural human-computer interface
Several emerging new applications of
wearable biometric sensor technology
BIoT Biometric gaming
Seamless consumer health

24. ©2015 Valencell, Inc. 24
In closing summary...
• The wearables market is exploding with opportunities, but there
is no one “killer app” emerging
• There are several reasons for this:
-- Diversity of needs & styles
-- Sensor functionality limitations
-- No sensor standards
-- Biology-based apps take more time to validate
-- The best value props are personalized
• But accurate biometric sensor data, in context of validated
physiological models, can be applied towards a suite of
continually compelling applications at scale

25. ©2015 Valencell, Inc. 25
Thanks for you time!
Valencell, Inc.
Dr. Steven F. LeBoeuf
Co-founder & President
www.valencell.com