Dino DNA! Health Identity from the Wrist @Jawbone
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Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1TjIbby. The Jawbone UP system captures health data through a battery of sensors on people's wrist and app on phones - movement, sleep, and heart rate. But this data is often incomplete and noisy, and most of the raw signals by themselves aren’t useful. Brian Wilt discusses how applied machine learning techniques and data science helped Jawbone build a successful fitness tracking app. Filmed at qconsf.com. Brian Wilt is Head of Data Science and Engineering at Jawbone.
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Dino DNA! Health Identity from the Wrist @Jawbone
- 1. BINGO! DINO DNA! HEALTH IDENTITY FROM THE WRIST BRIAN WILT HEAD OF DATA SCIENCE AND ANALYTICS @BRIANWILT
- 2. InfoQ.com: News & Community Site • 750,000 unique visitors/month • Published in 4 languages (English, Chinese, Japanese and Brazilian Portuguese) • Post content from our QCon conferences • News 15-20 / week • Articles 3-4 / week • Presentations (videos) 12-15 / week • Interviews 2-3 / week • Books 1 / month Watch the video with slide synchronization on InfoQ.com! http://www.infoq.com/presentations /machine-learning-jawbone
- 3. Purpose of QCon - to empower software development by facilitating the spread of knowledge and innovation Strategy - practitioner-driven conference designed for YOU: influencers of change and innovation in your teams - speakers and topics driving the evolution and innovation - connecting and catalyzing the influencers and innovators Highlights - attended by more than 12,000 delegates since 2007 - held in 9 cities worldwide Presented at QCon San Francisco www.qconsf.com
- 4. NOV 16 2015
- 5. “THEY TOLD ME TO SLEEP NORMAL”
- 9. UP3
- 10. • SCIENCE • PERSONALIZATION • MOTIVATION ROLE OF DATA+ML AT JAWBONE
- 11. SCIENCE: UNDERSTANDING THINGS NEVER BEFORE SEEN
- 12. 0% 25% 50% 75% 100% 9p 10p 11p 12a 1a 2a 3a 4a 5a 6a 7a 8a 9a 0-15 mi 0-25 mi 25-50 mi 50-75 mi 75-100 mi NAPA, SONOMA SAN FRANCISCO, OAKLAND NAPA, SONOMA, BERKELEY SOUTH NAPA EARTHQUAKE
- 13. HEALTH WITH SHARED CONTEXT IN REAL TIME
- 14. PERSONALIZATION: AN EXPERIENCE THAT KNOWS YOU
- 15. YouTube was abuzz with viral videos of small children — yet to speak, read or write — “pinching” magazine articles with their fingers as they would an iPad. These children were heralded as […] “digital natives”. [Now is a] new revolution, this time of “data natives” who expect their world to be “smart” and seamlessly adapt to them and their taste and habits. RISE OF THE DATA NATIVES MONICA ROGATI
- 17. MOTIVATION: A SMART COACH HELPING YOU LIVE HEALTHIER
- 18. HEALTHY WEIGHT FEMALE MALE 7.2 HRS SLEEP 6.2 7 6.8 6.6 6.4 WEIGHT AFFECTS SLEEP 19 23 27 31 35 39 43 47 (BMI)
- 19. logged 75% more meals per week logged 60% more workouts per week beat their step goal 43% more often averaged 17% more steps per day logged 16% more weigh-ins per week UP USERS WITH MAJOR WEIGHT LOSS
- 20. IDENTITY BIOMETRICSACTIVITIES A COMPLETE PICTURE OF YOU FULLY CONTEXTUALIZED 24 7 DATA
- 21. HOW DO WE MAKE THIS HAPPEN?
- 23. DATA PRODUCT ALL-STARS NEWS FEED PEOPLE YOU MAY KNOW PAGERANK RELATED ITEMS
- 24. DATA PRODUCT ALL-STARS NEWS FEED PEOPLE YOU MAY KNOW PAGERANK RELATED ITEMS CTR
- 25. CHRONIC DISEASE CARE IS 86% OF US HEALTHCARE COSTS DIABETES $176 BN 🍰 CARDIOVASCULAR $193 BN 💔 CANCER $157 BN 🚬 OBESITY $147 BN 🍟 67% OF GYM MEMBERSHIPS GO UNUSED
- 26. HOW WILL WE MAKE HEALTH DATA PRODUCTS?
- 27. DATA SCIENCE FOR THE LITTLE GUY
- 28. DATA SCIENCE FOR THE LITTLE GUY PROBLEM LARGE COMPANY SMALL COMPANY PEOPLE Where do I put my data science team? Vertical/horizontal organization? Only one or two people LOL PRODUCT Established Nascent DATA A mess A mess ML Deep on solving a problem, predictive Understanding and interpreting
- 29. DATA SCIENCE FOR HARDWARE SIGNALS Raw + Rich Compressed Limited CONTEXT Limited Sensor fusion Historical + Population USERS Single Single Aggregate LATENCY Seconds Minutes Slow COMPUTE Limited Powerful HAL 9000 DEPLOYMENT Months Weeks Hours
- 30. Awesome! Looks like you racked up some steps. What were you up to? BASKETBALL TENNIS ZUMBA +5,972 steps | +50%of goal 54m activity Edit ACTIVITY CLASSIFICATION VERSION 0 MOST COMMON WORKOUT 58% ACCURACY VERSION 2 SERVER-SIDE MODEL 85% ACCURACY VERSION 1 LAST WORKOUT 65% ACCURACY VERSION 3 BAND-BASED MODEL 99% ACCURACY
- 31. THE FIRMWARE HAS TO WORK THE FIRST TIME
- 32. TESLA HAS TO DELIVER THE CAR FIRST
- 35. WE CAN GET EXPONENTIALLY MORE FROM THE WRIST UP 2011 Accelerometer UP3 2015 Accelerometer Temperature GSR Heart Rate
- 36. WE CAN GET EXPONENTIALLY MORE FROM THE WRIST UP 2011 Accelerometer UP3 2015 Accelerometer Temperature GSR Heart Rate GERMAN PEDOMETER 1590
- 37. THE IPHONE MOMENT IS COMING
- 38. SLEEP 330M STEPS 4.1T MEALS 270M WORKOUTS 100M
- 39. HEALTH DATA IS WIDE
- 40. HEALTH DATA IS WIDE
- 41. BUT WIDE DATA TAKES MORE CLEANING • CLEAN EARLY AND OFTEN • APIS ARE HARD • BETTER TOOLS ARE NEEDED • ML IS NAIVE
- 42. 50 60 70 80 11:0 0 A M 11:10 A M 11:20 A M 11:30 A M 11:40 A M 11:50 A M 12:0 0 PM 12:10 PM 12:20 PM 12:30 PM CONTEXT IS KING EMPLOYEE HEART RATE DURING LAST JAWBONE ALL HANDS
- 43. SCALE GRACEFULLY
- 44. BEDTIME
- 45. BEDTIME
- 46. 16 MIN
- 47. SLEEP RECOVERY
- 48. BUT WHAT ABOUT DATA WE CAN’T GET
- 49. • API INTEGRATIONS • BAYESIAN INFERENCE • PUBLIC WHO/CENSUS DATA • USER DATA • UNSTRUCTURED TEXT • CREATIVITY • HIRING
- 50. DATA PRODUCTS SCALE HUMAN JUDGMENT. THEY ALSO SCALE AND REINFORCE HUMAN BIAS. @CLARECORTHELL
- 51. Tokyo office workers slept at least 30 minutes less than their counterparts in New York, Paris, Shanghai and Stockholm each night, averaging six hours, or 14 percent less than the recommended minimum, a study said. The study surveyed 180 men and women from each of the five cities. TOKYO’S WORKERS GET LESS SLEEP THAN US, ASIAN COUNTERPARTS KANOKO MATSUYAMA (2011)
- 52. datazoo=# SELECT gender, COUNT(*), AVG(total_hrs) datazoo-# FROM up_mod.sleep_clean_night datazoo-# WHERE gender IS NOT NULL GROUP BY gender; gender | count | avg --------+-----------+--------- M | 147284397 | 6.77170 F | 165579431 | 7.11274 Time: 1333.536 ms DATA DEMOCRATIZATION IS TABLE STAKES
- 53. STREAM API EVENTING USER DATA DYNAMO REDSHIFT ANALYTICS DATA PRODUCTS PIPE
- 55. NOT JUST ANALYTICS — DEMOCRATIZE DATA PRODUCTS
- 57. Not today Early to Bed Smart Coach noticed that you’ve been turning in late recently. Remember, your brain needs plenty of pillowtime to sort new information. Get in bed by 10:50pm tonight? SMART COACH I’m in
- 58. Not today Early to Bed Smart Coach noticed that you’ve been turning in late recently. Remember, your brain needs plenty of pillowtime to sort new information. Get in bed by 10:50pm tonight? SMART COACH I’m inNot today Early to Bed Smart Coach noticed that you’ve been turning in late recently. Remember, your brain needs plenty of pillowtime to sort new information. Get in bed by 10:50pm tonight? SMART COACH I’m in
- 59. UP UP slide to view now Today’s move summary: 10,518 steps, covering 5.1 mi, burning 1,518 cal. That’s 105% of your goal. SMART COACH GUIDES YOUR SLEEP Not today Early to Bed Smart Coach noticed that you’ve been turning in late recently. Remember, your brain needs plenty of pillowtime to sort new information. Get in bed by 10:50pm tonight? SMART COACH I’m in
- 60. UP UP slide to view now Today’s move summary: 10,518 steps, covering 5.1 mi, burning 1,518 cal. That’s 105% of your goal. 5 DAY OCT 23 STREAK Averaging 7h 6m per night Not today Early to Bed Smart Coach noticed that you’ve been turning in late recently. Remember, your brain needs plenty of pillowtime to sort new information. Get in bed by 10:50pm tonight? SMART COACH I’m in SMART COACH GUIDES YOUR SLEEP
- 61. UP UP slide to view now Today’s move summary: 10,518 steps, covering 5.1 mi, burning 1,518 cal. That’s 105% of your goal. 5 DAY OCT 23 STREAK Averaging 7h 6m per nighten our ort pm I’m in SMART COACH GUIDES YOUR SLEEP
- 62. Not today Early to Bed Smart Coach noticed that you’ve been turning in late recently. Remember, your brain needs plenty of pillowtime to sort new information. Get in bed by 10:50pm tonight? SMART COACH I’m in 23MMORE SLEEP, COMPARED TO THE CONTROL GROUP 72%INCREASED LIKELIHOOD OF BEATING THEIR SLEEP GOAL UP HELPS USERS SLEEP MORE
- 63. ANDRE IGOUDALA FINALS MVP • CONSISTENT BEDTIME • PUSH FOR LONGER SLEEP DURATION • TOOLS TO HELP FALL ASLEEP • STRATEGIC CAFFEINE USAGE • NAPPING RECOMMENDATIONS SLEEP COACHING • HOME: SLEEPS 5H 48M • AWAY: SLEEPS 6H 15M • DIFFICULTIES - FALLING ASLEEP AFTER GAME - STAYING ASLEEP • NAPS SOMETIMES • DRINKS CAFFEINE BEFORE AND DURING GAMES SLEEP PROFILE
- 64. +29%POINTS PER MINUTE SLEEP HELPED ANDRE’S OFFENSE FREE THROW +8.9% SLEEP IMPROVED ANDRE’S 3P% BY 2.18X >8 HRS <7 HRS 3 POINT PERCENTAGE 0.545 7-8 HRS 0.462 0.250 SLEEP HELPED ANDRE’S GAME -37%TURNOVERS PER FOULS -45%
- 65. • WE DO BIG THINGS AT SMALL COMPANIES • MORE IMPORTANT TO FIGURE OUT WHAT TO DO THAN DO IT • DATA SCIENCE FOR “GOOD” • IMPROVE HEALTH WITH BIG DATA TOOLS • USER EXPERIENCE COMES FIRST • THE FIRST ML MODEL ISN’T A MODEL • DEMOCRATIZE DATA PRODUCTS CONCLUSIONS
- 66. BINGO! DINO DNA! HEALTH IDENTITY FROM THE WRIST BRIAN WILT HEAD OF DATA SCIENCE AND ANALYTICS @BRIANWILT
- 67. Watch the video with slide synchronization on InfoQ.com! http://www.infoq.com/presentations/machine- learning-jawbone