ABOUT ME

(WHO IS THIS DUDE?)

NIMA BANAI

ROLE: SPECIAL PROJECTS(SOUNDS WAY COOLER THAN IT ACTUALLY IS…)

BACKGROUND: ELECTRICAL AND MECHANICAL ENGINEERING(BASICALLY A GLORIFIED NERD)

SRIDHAR IYENGAR SONNY VU JOHN SCULLEY

FOUNDERS

SAN FRANCISCO INDUSTRIAL & UX DESGIN HARDWARE ENGINEERING

INVENT AND MANUFACTUREWEARABLE AND SMART HOME PRODUCTS

WHAT WE DO

SEOUL MANUFACTURING

SHENZHEN SOFTWARE ENGINEERING

HO CHI MINH CITY DATA SCIENCE

SOFTWARE ENGINEERING OPERATIONS

BEST DEVICE

“A REFERENCE DESIGN FOR FUTURE WEARABLES.”- NEW YORK TIMES (MAR 2014)

“TOP 10 MOST BEAUTIFUL PRODUCTS OF 2013.”- CNET (JAN 2014)

MISFIT SHINEAN ELEGANT ACTIVITY & SLEEP MONITOR

NOMINEE

FIRST PIECE OF ADVICE: PARTNER UP!

SHINE FEATURED BY APPLE

CONFIDENTIAL

COCA-COLA RED SHINE

CONFIDENTIAL 12

VICTORIA’S SECRET SHINE

SWAROVSKI SHINE

WORLD’S FIRST ENERGY HARVESTING WEARABLE

SOLAR CHARGING: ENERGY CRYSTAL

GREY JET TOPAZ CHAMPAGNE COCA-COLA RED

WINE SEA GLASS STORM CORAL

SHINE IN 9 COLORS & 20+ ACCESSORIES

ZEST WAVE REEF COCA-COLA RED

ONYX FUCHSIA FROST

MISFIT FLASHAN ACTIVITY & SLEEP MONITOR FOR EVERYONE

SLEEPING CYCLING SWIMMING

MISFIT SOFTWARE

Available in 17 languages.

MISFIT BOLTA BEAUTIFULLY INTELLIGENT LIGHT BULB

800+ LUMENS

60W EQUIVALENT

DIRECT FLASH-CONTROL

13 WATTS

BEAUTIFUL WARM WHITE

PART OF THE MISFIT FAMILY

MISFIT BOLT MISFIT FLASH

`` `

MISFIT BEDDIT:NEAR CLINICAL GRADE SLEEP MONITOR

SMART ALARM

SLEEP SOUNDS

SLEEP QUALITY

HEART RATE

RESPIRATION

SNORING

MISFIT HOME

INSIGHT

WHAT WE’RE AFTER

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

COMFORT POWERFASHION

ENDURING WEARING EXPERIENCE

ENDURING WEARING EXPERIENCE

POWER

DESIGN

UTILITY

DREAM

POWER MANAGEMENT

HARDWARE & ALGORITHM EFFICIENCY

CHARGING EXPERIENCE

POWER GENERATION

BATTERY DENSITY

POWER

?

TURN-AROUND TEST

?

HACK: BE EVERYWHERE

TURN-AROUND TEST 2.0

HIG

HLO

WM

OTI

VATI

ON

EASE OF USEHARD TO DO EASY TO DO

WILL NOT DO/USE

MIGHT DO/USETURN-AROUND TEST

WILL DO/USE ALL THE TIME

FOGG BEHAVIOR MODEL

MOTIVATION EASE OF USE

FU

NB

OR

ING

USE

R E

XPE

RIE

NC

E

LIFE RELEVANCENON-ESSENTIAL CRITICAL

LOW

HIG

HIN

TER

AC

TIO

N C

OM

PLEX

ITY

PASSIVENESSACTIVE USERINTERVENTION

NO USERINTERVENTION

LOW MOTIVATION

HIGH MOTIVATION

HARD TO USE

EASY TO USE

MORE DATA

BETTER ALGORITHMS

WHAT WE NEED TO GET THERE

BETTER DATA

MORE DATA

BETTER ALGORITHMS

WHAT WE NEED TO GET THERE

SENSORS CAN PROVIDE UNPRECEDENTED DATA AND INSIGHTS

NOW THAT WE HAVE MOBILE INTERNET

THESIS

...BUT THEY’RE NO GOOD IF THEY’RE NOT USED

MAKE SENSING AMBIENT

THESIS

MAKE SENSING AMBIENT

THESIS

IN ENVIRONMENT

LIMITED IN MOBILITY AND ACCURACY

BARRIER FOR IOT

ON BODY

LIMITED IN WEARABILITY

BARRIER FOR WEARABLES

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

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?

ACTIVITY-TRACKING WITH WEARABLES

• USER’S ACTIVITY:

• QUANTITY

• INTENSITY

• QUALITY

• DIFFERENTIATED AGAINST OTHER USERS

• DIFFERENTIATED AGAINST THE USER’S PREVIOUS RECORDS

PROBLEM

• 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

• 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

• DATA PROCESSING:

• DIGITAL FILTERING, FOURIER ANALYSIS, NOISE REDUCTION,

SMOOTHING, SUBSAMPLING, TRANSFORMATION, ETC.

• FEATURE REPRESENTATIONS:

• FEATURE LEARNING AND DISCOVERY TECHNIQUES

• FEATURE SELECTION TECHNIQUES

TOOLS

• 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

• RECOGNIZE THE ACTIVITY PERFORMED, AND THE WEARING POSITION

• PERFORMANCE: ON 1800 UNIQUE LABELED ACTIVITY SESSIONS:

EXAMPLE: ACTIVITY RECOGNITION

• 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

FUTURE RESEARCH

• 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

• 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

• 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

• 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

• 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

LESSONS LEARNED

HIRING

SKILLS, NOT JUST IQ.

WISDOM, NOT JUST EXPERIENCE.

CULTURAL FIT ABOVE ALL.

CULTURE

SET THE FOUNDATION EARLY. (HIRES 1-50)

HIRE AND FIRE BASED ON CULTURE.

WORK-LIFE INTEGRATION, NOT WORK-LIFE BALANCE.

LEADING

STAND UP FOR WHAT YOU BELIEVE.

HAVE STRONG OPINIONS, LOOSELY HELD.

EAT LAST, FLY COACH.

EXECUTION

BUILD A BUSINESS, NOT JUST A PRODUCT.

BE LEAN.

BUSINESS

GIVE FIRST, GET LATER.

“ALWAYS LEAVE MONEY ON THE TABLE

FOR THE OTHER SIDE.”

PUZZLE

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

THINKING OUTSIDE THE BOX

THANK YOU