Nima Banai works in special projects for a company that designs and manufactures wearable and smart home products. The company has offices in San Francisco, Seoul, Shenzhen, and Ho Chi Minh City that focus on industrial design, hardware engineering, manufacturing, software engineering, and operations. Some of the company's products include the Misfit Shine activity tracker, Misfit Flash activity tracker, Misfit Bolt smart light bulb, and Misfit Beddit sleep monitor. The company aims to develop products that can be worn all the time to collect more data from more users over longer periods of time.

1. ABOUT ME
(WHO IS THIS DUDE?)

2. NIMA BANAI
ROLE: SPECIAL PROJECTS
(SOUNDS WAY COOLER THAN IT ACTUALLY IS…)
BACKGROUND: ELECTRICAL AND MECHANICAL ENGINEERING
(BASICALLY A GLORIFIED NERD)

4. SRIDHAR IYENGAR SONNY VU JOHN SCULLEY
FOUNDERS

5. SAN FRANCISCO
INDUSTRIAL & UX DESGIN
HARDWARE ENGINEERING
INVENT AND MANUFACTURE
WEARABLE AND SMART HOME PRODUCTS
WHAT WE DO
SEOUL
MANUFACTURING
SHENZHEN
SOFTWARE ENGINEERING
HO CHI MINH CITY
DATA SCIENCE
SOFTWARE ENGINEERING
OPERATIONS

6. BEST DEVICE
“A REFERENCE DESIGN FOR FUTURE WEARABLES.”
- NEW YORK TIMES (MAR 2014)
“TOP 10 MOST BEAUTIFUL PRODUCTS OF 2013.”
- CNET (JAN 2014)
MISFIT SHINE
AN ELEGANT ACTIVITY & SLEEP MONITOR
NOMINEE

9. FIRST PIECE OF ADVICE: PARTNER UP!

10. SHINE FEATURED BY APPLE

11. CONFIDENTIAL
COCA-COLA RED SHINE

12. CONFIDENTIAL 12
VICTORIA’S SECRET SHINE

13. SWAROVSKI SHINE

14. WORLD’S FIRST ENERGY HARVESTING WEARABLE
SOLAR CHARGING:
ENERGY CRYSTAL

15. GREY JET TOPAZ CHAMPAGNE COCA-COLA
RED
WINE SEA GLASS STORM CORAL
SHINE IN 9 COLORS & 20+ ACCESSORIES

16. ZEST WAVE REEF COCA-COLA
RED
ONYX FUCHSIA FROST
MISFIT FLASH
AN ACTIVITY & SLEEP MONITOR FOR EVERYONE
SLEEPING CYCLING SWIMMING

17. MISFIT SOFTWARE
Available in 17 languages.

18. MISFIT BOLT
A BEAUTIFULLY INTELLIGENT LIGHT BULB
800+ LUMENS
60W EQUIVALENT
DIRECT FLASH-CONTROL
13 WATTS
BEAUTIFUL WARM WHITE

19. PART OF THE MISFIT FAMILY
MISFIT BOLT MISFIT FLASH
`
`
`

20. MISFIT BEDDIT:
NEAR CLINICAL GRADE SLEEP MONITOR
SMART ALARM
SLEEP SOUNDS
SLEEP QUALITY
HEART RATE
RESPIRATION
SNORING

21. MISFIT HOME

22. INSIGHT
WHAT WE’RE AFTER

23. WEARABLES 1.0 WEARABLES 2.0
SENSING MEDICAL SMARTWATCH SAFETY IDENTIFICATION CONTROLS
ACTIVITY
HEART RATE
SLEEP
BRAIN SIGNALS
MEDICINE
GLUCOSE
ALERTS
GPS
MONITORING
EMERGENCY
LOGIN
PAY
LOCK
LIGHTS
GESTURES
HVAC
MUSIC
USE CASES

24. COMFORT POWERFASHION
ENDURING WEARING EXPERIENCE

25. ENDURING WEARING EXPERIENCE
POWER
DESIGN
UTILITY
DREAM

26. POWER
MANAGEMENT
HARDWARE &
ALGORITHM
EFFICIENCY
CHARGING
EXPERIENCE
POWER
GENERATION
BATTERY DENSITY
POWER

27. ?
TURN-AROUND TEST

28. ?
HACK: BE EVERYWHERE
TURN-AROUND TEST 2.0

29. HIGHLOWMOTIVATION
EASE OF USEHARD TO DO EASY TO DO
WILL NOT DO/USE
MIGHT DO/USE
TURN-AROUND TEST
WILL DO/USE ALL THE TIME
FOGG BEHAVIOR MODEL

30. MOTIVATION EASE OF USE
FUNBORINGUSEREXPERIENCE
LIFE RELEVANCENON-ESSENTIAL CRITICAL
LOWHIGHINTERACTIONCOMPLEXITY
PASSIVENESS
ACTIVE USER
INTERVENTION
NO USER
INTERVENTION
LOW MOTIVATION
HIGH MOTIVATION
HARD TO USE
EASY TO USE

31. MORE DATA
BETTER ALGORITHMS
WHAT WE NEED TO GET THERE

32. BETTER DATA
MORE DATA
BETTER ALGORITHMS
WHAT WE NEED TO GET THERE

33. SENSORS CAN PROVIDE UNPRECEDENTED DATA AND INSIGHTS
NOW THAT WE HAVE MOBILE INTERNET
THESIS

34. ...BUT THEY’RE NO GOOD IF THEY’RE NOT USED
MAKE SENSING AMBIENT
THESIS

35. MAKE SENSING AMBIENT
THESIS
IN ENVIRONMENT
LIMITED IN MOBILITY AND
ACCURACY
BARRIER FOR IOT
ON BODY
LIMITED IN WEARABILITY
BARRIER FOR WEARABLES

36. WORN ALL THE TIME
X
FOR A LONG TIME
X
BY A LOT OF PEOPLE
GREAT WEARABLE PRODUCTS
CONTINUITY
BETTER
DATA
VOLUME
DIVERSITY
ENDURING WEARING EXPERIENCE
ENGAGING
UX
DREAM

37. BRAND / TRIBE ASSOCIATION
COMFORT / PROTECTION
AESTHETICS / FASHION
BECAUSE...
SECONDARY FUNCTION
DATA
IT MAKES ME LOOK GOOD
IT MAKES ME FEEL COOL /
LIKE I BELONG
IT’S USEFUL
I LEARN THINGS I NEVER KNEW
IT MAKES ME FEEL SAFE
WHY DO / WOULD PEOPLE WEAR THINGS?

38. ACTIVITY-TRACKING WITH WEARABLES

39. • USER’S ACTIVITY:
• QUANTITY
• INTENSITY
• QUALITY
• DIFFERENTIATED AGAINST OTHER USERS
• DIFFERENTIATED AGAINST THE USER’S PREVIOUS RECORDS
PROBLEM

40. • CORRELATE ACTIVITY:
• USING NOISY ACCELEROMETER DATA
• CAPTURING SIGNALS OF PARTIAL-BODY MOTION
• CONTINUOUSLY CHANGING COORDINATE SYSTEM
• MINIMIZE POWER CONSUMPTION:
• HIGHLY OPTIMIZED ON-DEVICE ALGORITHMS AND DATA
TRANSMISSION PROTOCOLS
CHALLENGES

41. • MINIMIZE SIZE FOR WEARABILITY:
• LIMITED HARDWARE FLEXIBILITY (CHIP, MEMORY, SENSORS, ETC.)
• CONTEXT INDEPENDENT:
• NO CONTEXTUAL KNOWLEDGE OF USAGE (LOCATION, TIME,
TEMPERATURE, SOCIAL SETTING, ETC.)
• DATA COLLECTION:
• COLLECT ESSENTIAL, HIGHLY DIVERSE DATA FOR VARIOUS
ACTIVITY CATEGORIES
CHALLENGES

42. • DATA PROCESSING:
• DIGITAL FILTERING, FOURIER ANALYSIS, NOISE REDUCTION,
SMOOTHING, SUBSAMPLING, TRANSFORMATION, ETC.
• FEATURE REPRESENTATIONS:
• FEATURE LEARNING AND DISCOVERY TECHNIQUES
• FEATURE SELECTION TECHNIQUES
TOOLS

43. • BUILDING MODELS FROM TRAINING DATA (LEARNING ALGORITHMS):
• DISCRIMINATIVE LEARNING: K-NN, LINEAR, LOGISTIC, NON-LINEAR
REGRESSION, SVMS, ENSEMBLES
• GENERATIVE LEARNING: K-MEANS, EM, DIRICHLET PROCESS, BAYES
METHODS, DENSITY ESTIMATION TECHNIQUES
• TIME SERIES ANALYSIS: AUTOREGRESSIVE, DTW, HMM, AND OTHER
BAYESIAN NETWORKS
• INFERENCE ALGORITHMS:
• ACTIVITY DETECTION, RECOGNITION, QUANTIFICATION, AND ANALYSIS
• ITERATIVE IMPROVEMENT:
• ANALYZE ERRORS OF THE PREVIOUS ITERATION TO UPDATE THE NEXT
TOOLS

44. • RECOGNIZE THE ACTIVITY PERFORMED, AND THE WEARING POSITION
• PERFORMANCE: ON 1800 UNIQUE LABELED ACTIVITY SESSIONS:
EXAMPLE: ACTIVITY RECOGNITION

45. • DETECT AND CLASSIFY ARM GESTURES WITH A WRIST-WORN DEVICE.
(E.G.: WRIST-FLICKING FOR TIME CHECKING)
• CHALLENGES:
• ON-DEVICE, REAL-TIME ALGORITHM (SUCH AS TAPPING THE DEVICE OR
FLICKING THE WRIST)
• AVOID FALSE POSITIVES: GESTURE MOTION DATA ARE VERY SIMILAR TO
DAILY ACTIVITY DATA
• PERFORMANCE: IN THE RANGE OF 80-90% FOR PRECISION
• BEST RESULTS ARE ACHIEVED BY FUSING SENSORS AND ADAPTING
MODELS TO INDIVIDUALS
EXAMPLE: ARM GESTURE RECOGNITION

46. FUTURE RESEARCH

47. • ACTIVITY MONITORING:
• WHAT PEOPLE ARE DOING?
• DATA ANALYTICS:
• HOW ARE PEOPLE DOING (IT)?
• SMART-HOME SOLUTION:
• HOW TO REACT INTELLIGENTLY TO WHAT PEOPLE ARE DOING
SOME FUTURE DIRECTIONS

48. • ON-BODY MONITORING
• ACTIVITIES:
• MEDICAL APPLICATIONS (FALL DETECTION, SLEEP DISORDER
DETECTION)
• SPORT/TRAINING APPLICATIONS (GOLF, TENNIS, SWIMMING)
• HEALTH INDICATORS:
• RESPIRATION
• BLOOD PRESSURE
• HEART-RATE MONITORING
• STRESS
ACTIVITY MONITORING

49. • OFF-BODY MONITORING
• EXTERNAL SENSORS:
• SLEEP, WEIGHT
• FOOD / DRINK QUALITY
• VIDEO/IMAGE BASED:
• ACTIVITY DETECTION
• FOOD DETECTION
• TEMPERATURE, RESPIRATION, BLOOD PRESSURE, HEART-RATE
ACTIVITY MONITORING

50. • APPLICATIONS:
• TRENDS AND ABNORMALITY DETECTION
• INSIGHTS FOR BUSINESS
• PREDICTION FOR SUPPORT (PREVENTATIVE CARE)
• TECHNOLOGIES:
• BIG-DATA FOR UNDERLYING INFRASTRUCTURE
• CLOUD FOR EXTERNAL SERVICE
DATA ANALYTICS

51. • APPLICATIONS:
• LIFESTYLE AND ENTERTAINMENT (SMART LIGHT, MUSIC)
• HEALTHCARE (SLEEP, TRAINING)
• SECURITY
• TECHNOLOGIES:
• RELIABLE ACTIVITY TRACKING (SENSORS, VIDEOS)
• SMART HOME DEVICES (REMOTE CONTROLLABLE, HIGHLY
CONFIGURABLE)
• STATE-OF-THE-ART IN HOUSE COMMUNICATION (BLE MESH, ZIGBEE)
SMART HOME

52. LESSONS LEARNED

53. HIRING
SKILLS, NOT JUST IQ.
WISDOM, NOT JUST EXPERIENCE.
CULTURAL FIT ABOVE ALL.

54. CULTURE
SET THE FOUNDATION EARLY. (HIRES 1-50)
HIRE AND FIRE BASED ON CULTURE.
WORK-LIFE INTEGRATION, NOT WORK-LIFE BALANCE.

55. LEADING
STAND UP FOR WHAT YOU BELIEVE.
HAVE STRONG OPINIONS, LOOSELY HELD.
EAT LAST, FLY COACH.

56. EXECUTION
BUILD A BUSINESS, NOT JUST A PRODUCT.
BE LEAN.

57. BUSINESS
GIVE FIRST, GET LATER.
“ALWAYS LEAVE MONEY ON THE TABLE
FOR THE OTHER SIDE.”

58. PUZZLE

59. GOAL: CONNECT ALL DOTS USING ONLY 4 STRAIGHT LINES
WITHOUT LIFTING OFF YOUR HAND

65. THINKING OUTSIDE THE BOX

70. THANK YOU