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Activity Recognition from User-Annotated Acceleration Data Ling Bao and Stephen S. Intille

Activity Recognition from User-Annotated Acceleration Data Ling Bao and Stephen S. Intille. Presented by: Hong Lu. Key Questions. Can low cost wearable sensors be used for robust, real- time recognition of activity?

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Activity Recognition from User-Annotated Acceleration Data Ling Bao and Stephen S. Intille

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  1. Activity Recognition from User-Annotated Acceleration Data Ling Bao and Stephen S. Intille Presented by: Hong Lu

  2. Key Questions • Can low cost wearable sensors be used for robust, real- time recognition of activity? • Can training data be acquired from the end user without researcher supervision? • Does recognition require user-specific training data? • Do more sensors improve recognition?

  3. Data Collection • 13 ♂ + 7♀ = 20 subjects , age from 17 to 48 • 20 everyday activities • Subjects unsupervised when generating own training data, both in and outside the lab • What’s the problem of typical laboratory data? WHY? • Often data in lab is collected from researchers as subjects • Lab environments may restrict activity, simplifying recognition ! • Making researchers to label training examples does not scale Recognition rates highly depended on how data is collected 95.6% (laboratory data) VS 66.7% (naturalistic settings)

  4. Data Collection • What’s an accelerometer ? • An accelerometer is a device that measures the vibration, or acceleration of motion of a structure.

  5. Why Accelerometer ? • Many daily activities involve repetitive physical motion of the body or specific postures • E.g. Walking, Running, Scrubbing, Vacuuming • Low cost, tiny, energy efficient • Watch • Phone, mp3 player • Camera • computer • Game controller, the wii remote

  6. Sensor Placement • 5 wireless sensors • Right hip • Wrist • upper arm • Ankle • Thigh • Shack to synchronize

  7. Raw Data

  8. Features • Why we need them ? • Summarize the data bin • Capture useful information • What is the desired characteristics of a good feature ? • removing irrelevant noise • keeping relevant attributes to tell the difference • easy to compute ?

  9. Features • 512 sample windows (6.7s ?), 50% window overlap • Features: • Mean • Energy • Frequency-domain entropy • Correlation Between x, y accelerometer axes each board Between all pair wise combinations of axes on different boards

  10. Classifiers • Tested on decision table, nearest neighbor ( IBL), C4.5 decision tree, and naïve Bayesian classifiers • Machine Learning Toolkit (Witten & Frank, 1999)

  11. Training • Method 1: User-specific training • Train on activity sequence data for each subject • Test on obstacle course data for that subject • Method 2: Leave-one-subject out training • Train on activity sequence and activity data for all subjects but one • Test on obstacle course data for left out subject • Average for all 20 subjects

  12. Results • C45 Decision tree wins • It shows • User-specific training: 71.6 ±7.4 • Leave-one-subject-out training: 84.3 ±5.2 • Why? • Commonalities between people may be more significant than individual variations • Larger training set

  13. Result • Overall, promising • Data collected by subjects themselves without supervision • Data collected both in and outside of laboratory setting • Poorer performance results when… • Activities involve less physically characteristic movements , Activities involve little motion or standing still • Activities involve similar posture/movement (e.g. watching TV, sitting and relaxing)

  14. The dark side • The more sensors you placed, the higher accuracy you may achieved, but … • cost • you look weird • hard to deploy • more computational horse power

  15. Accelerometer Discriminatory Power • Tested C4.5 classifier with using subsets of accelerometers: • Hip, wrist, arm, ankle, thigh, thigh and wrist, hip and wrist • Best single performers: • Thigh (-29.5%) • Hip (-34.1%) • Ankle(-37%)

  16. Accelerometer Discriminatory Power • With only two accelerometers get good performance: • Thigh and wrist (-3.3% compared with all 5) • Hip and wrist (-4.8% compared with all 5)

  17. Overview • The study • Activity recognition: 20 household activities • Sensors: 5 non-wired accelerometers • Data: participants labeled own data • Result • Good performance with decision tree classifier • Subject-specific training data for some activities may not be required • Reasonable accuracy can be achieved with only 2 of 5 accelerometers

  18. Thank you! The End For some slides, I used content of Emmanuel MunguiaTapia’s presentation

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