©2015 Valencell, Inc. 1

What if there is no killer app for wearables?...

IEEE Rock Stars of WearablesSeptember 2015

©2015 Valencell, Inc. 2

The Take-AwayThere 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.

©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.

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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

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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

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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

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Rich form-factor diversity in biometric wearables using PPG

Armbands

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Total Steps1130

Calories210

Cadence125

Heart Rate135

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 WearablesGiving you numbers

Next-Gen WearablesProviding you personalized direction

Next-generation user experiences (UX) are moving from simply reporting numbers to providing personalized direction

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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 Trackingor

Biometric Tracking

Activity Tracking +

Biometric Tracking

Activity Tracking +

Biometric Tracking +

Models & Contextual Analysis

Increasing mass-consumer value & increasing sensor accuracy requirements

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Cadence (Step Rate)

Time

Bea

ts(S

teps

)/Min

Accurate heart rate + cadence is criticalfor 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

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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]

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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

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• 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

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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 RateBlood

Pressure

Serial output standards would help scale sensor technology for use in multiple form-factors & consumer applications Valencell US Patent 8,923,941

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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

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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!”

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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.”

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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

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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:

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Accurately tracking biometrics + activity can expose positive fitness outcomes

Resting HRreduced by 10%

Cardiac Efficiency increased by 10%

VO2max didn’t change much

HR recovery rose & fell

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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-1target

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.

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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

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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

©2015 Valencell, Inc. 25

Thanks for you time!

Valencell, Inc.Dr. Steven F. LeBoeufCo-founder & President

www.valencell.com