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What if there is no killer app for wearables?

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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.

Published in: Devices & Hardware
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What if there is no killer app for wearables?

  1. 1. ©2015 Valencell, Inc. 1 What if there is no killer app for wearables?... IEEE Rock Stars of Wearables September 2015
  2. 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. 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. 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. 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. 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. 7. ©2015 Valencell, Inc. 7 Rich form-factor diversity in biometric wearables using PPG Armbands
  8. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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
  23. 23. ©2015 Valencell, Inc. 23 Natural human-computer interface Several emerging new applications of wearable biometric sensor technology BIoT Biometric gaming Seamless consumer health
  24. 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. 25. ©2015 Valencell, Inc. 25 Thanks for you time! Valencell, Inc. Dr. Steven F. LeBoeuf Co-founder & President www.valencell.com

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