NIMA BANAI
ROLE: SPECIAL PROJECTS(SOUNDS WAY COOLER THAN IT ACTUALLY IS…)
BACKGROUND: ELECTRICAL AND MECHANICAL ENGINEERING(BASICALLY A GLORIFIED NERD)
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
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 BOLTA BEAUTIFULLY INTELLIGENT LIGHT BULB
800+ LUMENS
60W EQUIVALENT
DIRECT FLASH-CONTROL
13 WATTS
BEAUTIFUL WARM WHITE
MISFIT BEDDIT:NEAR CLINICAL GRADE SLEEP MONITOR
SMART ALARM
SLEEP SOUNDS
SLEEP QUALITY
HEART RATE
RESPIRATION
SNORING
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
POWER MANAGEMENT
HARDWARE & ALGORITHM EFFICIENCY
CHARGING EXPERIENCE
POWER GENERATION
BATTERY DENSITY
POWER
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
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?
• 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
• 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
CULTURE
SET THE FOUNDATION EARLY. (HIRES 1-50)
HIRE AND FIRE BASED ON CULTURE.
WORK-LIFE INTEGRATION, NOT WORK-LIFE BALANCE.