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Activity Recognition Using Cell Phone Accelerometers. Jennifer Kwapisz, Gary Weiss, Samuel Moore Department of Computer & Info. Science Fordham University. We are Interested in WISDM. WISDM: WIreless Sensor Data Mining

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Activity recognition using cell phone accelerometers

Activity Recognition Using Cell Phone Accelerometers

Jennifer Kwapisz, Gary Weiss, Samuel Moore

Department of Computer & Info. Science

Fordham University

SensorKDD 2010

We are interested in wisdm
We are Interested in WISDM

  • WISDM: WIreless Sensor Data Mining

    • Powerful portable wireless devices are becoming common and are filled with sensors

    • Smart phones: Android phones, iPhone

    • Music players: iPod Touch

  • Sensors on smart phones include:

    • Microphone, camera, light sensor, proximity sensor, temperature sensor, GPS, compass, accelerometer

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Accelerometer based activity recognition
Accelerometer-Based Activity Recognition

  • The Problem: use accelerometer data to determine a user’s activity

  • Activities include:

    • Walking and jogging

    • Sitting and standing

    • Ascending and descending stairs

    • More activities to be added in future work

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Applications of activity recognition
Applications of Activity Recognition

  • Health Applications

    • Generate activity profile to monitor overall type and quantity of activity

    • Parents can use it to monitor their children

    • Can be used to monitor the elderly

  • Make the device context-sensitive

    • Cell phone sends all calls to voice mail when jogging

    • Adjust music based on the activity

  • Broadcast (Facebook) your every activity

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Our wisdm platform
Our WISDM Platform

  • Platform based on Android cell phones

    • Android is Google’s open source mobile computing OS

    • Easy to program, free, will have a large market share

  • Unlike most other work on activity recognition:

    • No specialized equipment

    • Single device naturally placed on body (in pocket)

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Our wisdm platform1
Our WISDM Platform

  • Current research was conducted off-line

    • Data was collected and later analyzed off-line

  • In future our platform will operate in real-time

  • In June we released real-time sensor data collection app to Android marketplace

    • Currently collects accelerometer and GPS data

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  • Included in most smart phones & other devices

    • All Android phones, iPhones, iPod Touches, etc.

    • Tri-axial accelerometers that measure 3 dimensions

  • Initially included for screen rotation and advanced game play

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Examples of raw data
Examples of Raw Data

  • Next few slides show data for one user over a few seconds for various activities

  • Cell phone is in user’s pocket

  • Earth’s gravity is registered as acceleration

  • Acceleration values relative to axes of the device, not Earth

    • In theory we can correct this given that we can determine orientation of the device

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Descending stairs
Descending Stairs

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Ascending stairs
Ascending Stairs

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Data collection procedure
Data Collection Procedure

  • User’s move through a specific course

    • Perform various activities for specific times

    • Data collected using Android phones

    • Activities labeled using our Android app

  • Data collection procedure approved by Fordham Institutional Review Board (IRB)

  • Collected data from 29 users

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Data preprocessing
Data Preprocessing

  • Need to convert time series data into examples

  • Use a 10 second example duration (i.e., window)

    • 3 acceleration values every 50 ms (600 total values)

  • Generate 43 total features

    • Ave. acceleration each axis (3)

    • Standard deviation each axis (3)

    • Binned/histogram distribution for each axis (30)

    • Time between peaks (3)

    • Ave. resultant acceleration (1)

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Final data set
Final Data Set

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Data mining step
Data Mining Step

  • Utilized three WEKA learning methods

    • Decision Tree (J48)

    • Logistic Regression

    • Neural Network

  • Results reported using 10-fold cross validation

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Summary results
Summary Results

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J48 confusion matrix
J48 Confusion Matrix

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  • Able to identify activities with good accuracy

    • Hard to differentiate between ascending and descending stairs. To limited degree also looks like walking.

    • Can accomplish this with a cell phone placed naturally in pocket

    • Accomplished with simple features and standard data mining methods

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Related work
Related Work

  • At least a dozen papers on activity recognition using multiple sensors, mainly accelerometers

    • Typically studies only 10-20 users

  • Activity recognition also done via computer vision

  • Actigraphy uses devices to study movement

    • Used by psychologists to study sleep disorders, ADD

  • A few recent efforts use cell phones

    • Yang (2009) used Nokia N95 and 4 users

    • Brezmes (2009) used Nokia N95 with real-time recognition

      • One model per user (requires labeled data from each user)

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Future work
Future Work

  • Add more activities and users

  • Add more sophisticated features

  • Try time-series based learning methods

  • Generate results in real time

  • Deploy higher level applications: activity profiler

SensorKDD 2010

Other wisdm research
Other WISDM Research

  • Cell Phone-Based Biometric identification1

    • Same accelerometer data and same generated features but added 7 users (36 in total)

    • If we group all of the test examples from one cell phone and apply majority voting, achieve 100% accuracy

    • Can be used for security or automatic personalization

  • Interested in GPS spatio-temporal data mining

1Kwapisz, Weiss, and Moore, Cell-Phone Based Biometric Identification, Proceedings of the IEEE 4th International Conference on Biometrics: Theory, Applications, and Systems (BTAS-10), September 2010.

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Thank you

Thank You


SensorKDD 2010