dataengconf sf16 - deriving meaning from wearable sensor data

Post on 15-Apr-2017

285 Views

Category:

Technology

1 Downloads

Preview:

Click to see full reader

TRANSCRIPT

DERIVING MEANING FROM WEARABLE SENSOR DATA

SAMEERA PODURI

@sameerapoduri

1530 pocket watch

wrist watch1810

digital watch1969

1530 pocket watch

wrist watch1810

digital watch1969mobile phone1973smartphone2008

2016 ?

• batteries shrinking

• small + low-power sensors, compute, comms

24/7 sensor data platforms!

DATA SCIENCE FOR WEARABLES: PERCEPTION & PERSONALIZATION

• Hardware is maturing• Sensor data is growing exponentially• Unlocking potential requires deriving meaning from data

BEAUTY + ENGINEERING IN SERVICE OF A BETTER LIFE

Measure steps, sleep states, workouts, heart rate

DynamoDB

Kinesis

Eventing Data

JB server

User Data

Platform

Redshift

Processing

DATA INFRASTRUCTURE

DETECTING ACTIVITY & SLEEP

PEDOMETER

PEDOMETER

theory

real data

GOT DATA?

1. Deploy a model2. Collect data3. Retrain model4. A/B Test 5. Repeat

PEDOMETER

Classifier

SLEEP DETECTION

SIGNALS Raw, rich Partly compressed, rich Compressed

CONTEXT Limited Sensor fusion History, population,weather, etc

USERS Single Single Aggregate

WHERE TO DEPLOY?

DATA PRODUCTS FOR HARDWARE

LATENCY Seconds Minutes Minutes+ Network delays

COMPUTE Limited Powerful

DEPLOYMENT Months Weeks Hours

WHERE TO DEPLOY?

Version 0 Most common workout 58% Accuracy

Version 1Last workout 15% lift

Version 2

WORKOUT CLASSIFICATION

PERSONALIZED INSIGHTS

• How can I understand this data? • How should I feel about what it tells me?• What action should I take in response?

Smart Coach RemembersRemember how you took 45,365 steps on July 4? Smart Coach remembers! On your health journey, don't forget to stop and celebrate.

Step UpdateSmart Coach noticed a surge in activity. In fact, you surpassed 9,690 steps, your typical 5:00pm average.

Last night you had 35m of REM sleep, less than the 1h9m that is typical for your age group. One way to improve your chances for more REM is to try an earlier bedtime than last night's 12:35am. You can set a bedtime Reminder for 11:35pm to help.

REM TimeLong Journey?Looks like you've been traveling recently, which can throw off your routine. Try setting a bedtime reminder for tonight to help you adjust.

Your daily average of 17,543 steps places you in the top 3% of UP females in their 30s. Bravo, Angela.

Welcome to the 3%

DynamoDB Redshift

User Facts

Insights

HEART RATE

“Your heart rate is 85 beats per minute.”

CONTEXT MATTERS

This morning’s resting heart rate was higher than 61bpm, your 30-day average. Dehydration may be the cause. If you think you were dehydrated last night, make up for it today with 8 glasses of water.

Start with Hydration

BEHAVIOR CHANGE

BEHAVIOR CHANGE

Commitment and Consistency

Source: Cialdini, R. B. (2009). Influence: Science and practice (5th edition). Boston, MA: Pearson Education.

BEHAVIOR CHANGE

Commitment and ConsistencyFoot In The Door TechniqueSource: Freedman, J.L. & Fraser, S.C. (1966). Compliance without pressure: The foot-in-the-door technique. Journal of Personality and Social Psychology, 4, 195-202.

BEHAVIOR CHANGE

Commitment and ConsistencyFoot In The Door Technique

Goldilocks TasksSource: Pink, Daniel (2009). Drive: The Surprising Truth About What Motivates Us. New York, NY: Riverhead Books.

BEHAVIOR CHANGE

Commitment and ConsistencyFoot In The Door Technique

Goldilocks Tasks

Source: Carpenter, Chris. (2013) A meta-analysis of the effectiveness of the "but you are free" compliance-gaining technique

Reactance

BEHAVIOR CHANGE

72%Increased likelihood to go to bed early enough to hit their

sleep goal

23mMinutes earlier to bed, compared to if they didn’t

receive a TIW

BEHAVIOR CHANGE

DATA STORIES

HUNDREDS OF MILLIONS NIGHTS OF SLEEP

TRILLIONS OF STEPS

HUNDREDS OF MILLIONS FOOD ITEMS

“The fact that the tracker measured my sleep and my activity level was a big part of my recovery. I had this way to ‘metric’ my body as I went through this. Sleep is so important in brain function anyway, and when you're recovering from a brain injury, it's even more important.”

PARTING THOUGHTS…

• Wearables will help us live healthier• Health data at unprecedented scale and granularity• Data Science can play a critical role in unlocking their potential

by deriving meaning from this sensor data

• Sensor and Accelerometer data• > 1GB/sec aggregated across users• Compacted on band into code-words

UP Band Phone

{ steps: 12, hr: 78, ts: 1455741797 ….. ….

}

• Phone adds context to band signals • Collects eventing and logging data from app• Eventing/Logging passes through Kinesis • User data is stored in the appropriate DB

Kinesis

Eventing Data

JB server

User Data

Phone ServerPlatform

• Run batch ETL jobs using Elastic MapReduce to clean and process data.

• Choose the appropriate processing framework depending on type of job (Hadoop/Spark)

• Store cleaned and anonymized data in Redshift.

Server Warehouse

Kinesis

Platform

Redshift

Processing

Aggregations

New tables of interest

ETL Pipeline

Analyze Fields

Redshift

Add columns

Load

Kinesis

Extract

Aggregations

New tables of interest

TransformCreate config

ANALYSIS AND EXPLORATION

WEARABLES FOR BETTER HEALTH

Chronic disease care is 86% of US healthcare cost

• Diabetes affects 12.3% population, costs $245B• Obesity affects 36% population, costs

top related