This document discusses integrating optical heart rate monitoring and biometrics into wearable devices. It provides an overview of the history of photoplethysmography and how recent innovations have enabled its use in wearables. Advanced sensor design and signal processing algorithms allow for accurate heart rate and other biometrics to be measured during various activities. Validation testing is important to ensure accuracy. Biometric data from wearables can provide insights into fitness and health.

1. Integrating Optical Heart
Rate and Biometrics Into
a Wearable Device
Ryan Kraudel
VP, Marketing

2. ©2016 Valencell. Inc
Photoplethysmography (PPG) – an ancient technology
recently revolutionized with wearable innovations
Late 1800s
1981
1990
2013
Real-time blood flow was 1st
assessed using early light bulbs
The first pulse oximeters were
launched for hospital use,
measuring pulse rate & blood
oxygen via 2 alternating LEDs
Sporadic motion artifact removal
was developed for finger pulse
oximeters in hospital settings
Continuous active motion artifact
removal was commercialized in
consumer PPG products (earbuds)
for use in everyday activitiesLate 1930s
The term
“photoplethysmography”
coined by scientists
2010
Passive PPG monitoring
via consumer armbands
Revolutionary innovations have made PPG wearable:
• Optomechanics that effectively couple light to/from the body
• Tight-integration into consumer form-factors
• Active noise removal using noise references & advanced DSP

3. ©2016 Valencell. Inc
Active Signal Characterization
Optical noise from skin motion, body
motion, & environmental noise (such as
sunlight) is actively removed from the
blood flow signals in real-time.
Motion tolerant optical heart rate sensing combines
advanced sensor design + active signal characterization
Highly Accurate
PPG Signal
PPG Sensor Systems
Years of R&D have resulted in advanced
optomechanical designs and integration
expertise that optimize sensor signal quality.
PerformTek vs. CSHRM
n = 40
R2 = 0.99

4. ©2016 Valencell. Inc
Peak Amplitude
(Pulse Pressure)
RRi
(HRV, Cardiac Functioning)
Breathing Rate
(Metabolic Status)
Perfusion Variation
Heart Rate
Highly accurate PPG is the foundation for
valuable derivative metrics
Active signal characterization
from PPG can accurately assess
numerous biometrics
• Heart Rate
• Breathing Rate
• Heart Rate
Variability (HRV)
• Cardiovascular
Fitness
• Blood Pressure
• Blood Oxygen
• Cardiac
Efficiency

5. ©2016 Valencell. Inc
Power
Efficiency
Form Factor
Diversity
Accuracy
Biometric
Diversity
Validation in
Multiple Activities
ECGOptical/PPG
Three key technologies are currently employed in consumer
heart rate monitors
Bioimpedance

6. ©2016 Valencell. Inc
Rich form-factor diversity in biometric
wearables using PPG
Armbands

7. ©2016 Valencell. Inc
Top 5 Challenges in OHRM
Source: “Optical heart-rate measurement’s top 5 challenges” Dr. Steven LeBoeuf; EDN Magazine; 8-25-15
Optical Noise Skin Tone Blood Perfusion
Sensor Location Crossover Problem

8. ©2016 Valencell. Inc
Cadence
(Step
Rate)
Time
Beats(Steps)/Min
Accurate heart rate + cadence is critical
for important fitness and health assessments…
Accurate HRM Mood Ring HRM
HRV
HR
Response
Peak HR/
VO2max
HR Recovery
Cardiac
Efficiency

9. ©2016 Valencell. Inc
Two key factors determine accuracy, reliability, & scalability…
and these cannot be designed independently
• Coupling
• Wavelengths
• Multiple form factors
• Multiple emitters
• Gross displacement
• Motion-tolerant
• Validation
• Performance
• Power management
• Scalable biometric
roadmap
Optomechanics
Signal Extraction
Algorithms

10. ©2016 Valencell. Inc
Extensive testing is critical to success
•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 that includes regression
analysis (R2) & Bland-Altman analysis
•Diagnosis analysis that includes true positives,
false positives, true negatives, false negatives, &
total positives & negatives
•Ideally, there must be independent validation of
each metric

11. ©2016 Valencell. Inc
Studying how biometrics change over time
• 6 week study of heterogeneous participant mix with varying fitness levels, skin
tones, genders, and 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
• Baseline sensors – CSHRM, indirect calorimetry, calibrated treadmill
• Device under test – Valencell earbuds reference design

12. ©2016 Valencell. Inc
By tracking biometrics + activity, fitness outcomes can be identified
Resting HR
reduced by 10%
Cardiac Efficiency
increased by 10%
VO2max
didn’t change much
HR recovery
rose & fell

13. ©2016 Valencell. Inc
Accurate biometric sensor data can also support
accurate health assessments
Assessment Definition What it means for fitness What it 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-mintute 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-mintute 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
[Chon, Hohnloser, McManus, Park, Valkama]

14. ©2016 Valencell. Inc
Valencell BenchmarkTM Sensor System
• Best-in-Class PerformTekTM sensor
solution with state-of-the-art
algorithms.
• Based on flagship reference
designs, new features are first
released on this design.
• Designed with customer manufacturing
in mind.
• Ultrasonic weld rib on the lens for a
water tight seal.
• Power and Communication across a
standard 20 pin connector; I2C or
UART bus
Biometric Monitoring Design & Integration
Ear ModuleWrist Module
Operating supply
2.1V - 3.3V Vdd for sensor module and
1.8Vmcu, <2mA ave operating; 16uA
Standby current
Connection
Solder pads on sensor; 0.4mm pitch
WLCSP
Data Interface UART or I2C options, POST, WAKE
pins
Sensor
Dimensions
(11.8 x 4.7 x 5.6) mm
Sensor Weight 0.25 grams
Operating
supply
1.71 - 3.6V VDD, 3.1 – 5.0V VLED
<2.1mA average operating
current , <5uA standby current
Connector
0.4mm pitch Hirose 20 pin FPC
receptacle
Data Interface
UART or I2C options, POST,
WAKE pins
Module
Dimensions
14.5 x 19.5 x 3.25 mm
Weight 0.85 grams

15. ©2016 Valencell. Inc
Accelerate development and testing for
highly accurate wearables
• STM and Valencell have partnered to create a dev platform
for highly accurate wearables
• Combines ST’s SensorTile (www.st.com/sensortile) with
Valencell’s Benchmark optical biometric sensor system
• Kit includes everything needed to remotely sense and
measure biometric, motion, environmental and acoustical
parameters.

16. ©2016 Valencell. Inc
Wearables dev kit features
SensorTile Development Kit for connectable sensor nodes
STEVAL-STLKT01V1
• STM32L476 – 32-bit ultra-low-power MCU with CortexM4F
• LSM6DSM – iNEMO inertial module: 3D accelerometer and
3D gyroscope
• LSM303AGR – Ultra-compact high-performance eCompass
module: ultra-low power 3D accelerometer and 3D
magnetometer
• LPS22HB – MEMS nano pressure sensor: 260-1260 hPa
absolute digital output barometer
• MP34DT04 – 64dB SNR Digital MEMS Microphone
• BlueNRG-MS – Bluetooth low energy network processor BT
4.1
• BALF-NRG-01D3 – 50 Ω balun with integrated harmonic
filter
• LD39115J18R – 150 mA low quiescent current low noise
LDO 1.8 V
• BLUEMICROSYSTEM2 STM32Cube expansion software
package, supporting different algorithms tailored to the on-
board sensors
• ST BlueMS: iOS and Android demo apps
• Compatible with STM32 ecosystem through STM32Cube
support
Included in kit package:
- Valencell BenchmarkTM sensor system integrated
to SensorTile
- 3 LED’s – 2 green, 1 yellow
- Photodetector
- STM LIS2DH accelerometer
- SMT32F401 processor
Software
- Valencell’s biometric signal processing algorithms
utilizing active signal characterization
- Valencell’s biometric assessment algorithms to
process heart rate, cardiac efficiency, VO2, RR
interval (heart rate variability), and much more.

17. ©2016 Valencell. Inc
In Summary...
• The wearables market is exploding with opportunities
• Highly accurate optical heart rate sensor systems can support a wide variety of
use cases and form factors
• However, when designing a new product, the optomechanics, signal extraction
methodology, and clinical validation are critical
• If designed correctly, motion-tolerant OHRM can deliver a long list of insightful
biometrics that support current and future roadmap needs

18. Integrating Optical Heart
Rate and Biometrics Into
a Wearable Device
Ryan Kraudel
VP, Marketing
kraudel@valencell.com
919-747-3668