Clinical Validation of Biometric Wearables and Applying Accurate Biometrics Towards Compelling User Experiences

Clinical Validation of Biometric Wearables
and Applying Accurate Biometrics Towards
Compelling User Experiences
© 2018 Valencell, Inc
Chris Eschbach, PhD
July 2018

Overview: Biometrics based data can be used
for the purposes of providing interesting and
meaningful lifestyle, health, and fitness insights.
Including, but not limited to, assessments of health
and fitness, quantification of daily living and training.

Outline
• Valencell overview
• Data examples for accuracy
• Biometric wearable sensors and validation
• Use cases

Valencell
A systems approach for Valencell:
• A sensor system- Benchmark, licensee, patent
• Optomechanics, firmware, algorithms, and testing & validation
processes.

Biometrics laboratory operations & staff
Operations
• In session trials (R & D +
validation): 60 per week
• Other research trials (longitudinal
and rest): 30 per week
Staff
• Director
• Operations lead
• Data lead
• Reports lead
• Trials specialists

Avg HR= 149
Calories for 55 min= 682
0
1
2
3
4
5
6
7
8
9
10
0
20
40
60
80
100
120
140
160
180
200
3
159
315
471
627
783
939
1095
1251
1407
1563
1719
1875
2031
2187
2343
2499
2655
2811
2967
3123
3279
Speed(mi/hr)
HeartRate(B/min):Cadence(steps/min)
Time (sec)
55 min: WU + 3x10 intervals
Optical_HR_S8
Speed
HR= 158
VO2max= 43
Data examples for accuracy

0
1
2
3
4
5
6
7
8
9
10
0
20
40
60
80
100
120
140
160
180
200
3
159
315
471
627
783
939
1095
1251
1407
1563
1719
1875
2031
2187
2343
2499
2655
2811
2967
3123
3279
Speed(mi/hr)
HeartRate(B/min):Cadence(steps/min)
Time (sec)
55 min: WU + 3x10 intervals
CHSRM_S8
Optical_HR_S8
Speed
Avg HR= 161
Calories for 55 min= 764
HR= 183
VO2max= 39

Avg HR= 7.5% difference
Calories for 55 min=
11.7% difference
HR= 13.6%
VO2max= 10.2%

The industry lacks a clear method of benchmarking sensor
accuracy…damaging reputation of the industry and allowing poor
quality sensors to flood the market and erode prices.
If industry awareness of what constitutes a good fitness tracker does
not improve, the fitness tracking industry will be a low-quality, low-value
industry
ABI Research. (February 19, 2015). Hot Tech Innovators. Retrieved from https://www.abiresearch.com/whitepapers/Hot-Tech-Innovators/

CTA standards
• Consumer Technology Association- Health Fitness & Wellness Standards
• CTA-2065 – Physical Activity Monitoring for Heart Rate
• Participants, protocols (5), Data performance: output rate, availability,
accuracy, offset
• Physical Activity Monitoring for Fitness Wearables: Step Counting
• Definitions and Characteristics for Wearable Sleep Monitors
• Methodology of Measurement for Features in Sleep Tracking Consumer
Technology Devices and Applications
• Consumer Stress Monitoring Technologies, Activity intensity, mobile health
applications
https://cta.tech/Research-Standards/Standards-Listing/Health-Fitness-Technology-Standards.aspx

Three key use cases, but no “killer app”
Lifestyle In-Session Health Monitoring

Substantial form-factor diversity in biometric
wearables using PPG
Armbands

Data- metrics and devices
• Metrics collection
• Raw data
• Heart rate, RRi, steps, blood pressure, etc.
• Calories, VO2, Speed, Distance, etc.
• Baseline devices
• Chest strap HRM, ECG, Video, Manual and
automatic BP
• Indirect calorimetry, measured courses, and
calibrated treadmills

Data - laboratory tools
• Wide range of participants
• Experienced staff
• Databases and data experts to overcome data collection
and UX challenges
• Data collectors (Raw, multi BLE, single BLE): commercial
applications and custom
• Exercise equipment
• Treadmills, bikes, strength, lifestyle, etc
• Reference devices (baseline and procedures for them)
• Collection devices: phones, cameras, programs, computers

Data collection process – areas of consideration
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

Data collection process - protocols

Data collection process - protocols
• Various field trials beyond the foundational protocol (8-min dynamic treadmill)
• Sunlight testing
• Lifestyle (typical daily movements)
• Resting heart rate
• Dynamic bike indoor (10-minutes)
• Running intervals (15-minutes)
• Free run/bike (>20 minutes)
• Rowing ergometer (13-minutes)
• Strength circuit (typical strength exercises)
• High intensity interval strength
• Swimming

Data collection process - laboratory tools
Reference devices (baseline and procedures for them)

Data analysis and reporting
• Statistical analysis
• mean ± SD (when appropriate)
• bias as percent error
• mean absolute percent error (MAPE)
• frequency distribution within ±5% windows
• Utilize thresholds: 85% of data will be within ±5%
• Bland-Altman and correlation as needed
• accuracy related to heart rate intensity (≤ moderate and ≥ vigorous)

Data analysis and reporting – areas for consideration
• Baseline errors (remove but report
errors)
• Time for HR lock
• Data alignment
• Latency

©2018 Valencell. Inc
Example 1
 Quantification of activity intensity
 To improve overall cardiovascular health, the American Heart Association
recommends at least 150 minutes of moderate exercise or 75 minutes of
vigorous exercise per week – or a combination of the two for adults.
 Inputs and outputs
 Intensity can be quantified in several ways including heart rate,
accelerometry, and rating of perceived exertion.
 Allows for going beyond simply counting “10,000 steps”

Example 2
 Distribution and amount of “activity” volume and intensity to optimize
performance in both daily living and performance
 Research demonstrates a nonlinear relationship to benefits of intensity and
volume of activity. Quantification of those metrics allow for visualization of
their benefit to health and performance beyond AHA guidelines

Example 3
 Overall stress status
 Day to day and week to week variation in heart rate variability allows for
tracking of overall stress from a variety of sources.

Example 4
 Estimating a cardiac age by means of heart rate variability inputs and outputs
 Observed significant correlations between the HRV descriptors and age has
allowed researchers to envisage a general empirical index of the cardiac
age.

Example 5
 Estimation of human core temperature
 Use of biometric data to estimate core temperature has demonstrated its
use is accurate enough to provide practical indication of thermal work
 Output of core temperature is beneficial to workers, military, and athletes
in that research has clearly demonstrated correlation between
temperature and ability for work output

Example 6
 Diet planning based on activity metrics
 Knowledge of previous and upcoming trends allows for recommendations to
diet that go beyond total calories.

Key takeaways
• Accuracy is critical for a rewarding user experience in all use cases
• Validation takes planning, experience, and intuition
• Use cases should be simple to follow and supported by research

Chris Eschbach
eschbach@valencell.com
www.valencell.com

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