Your SlideShare is downloading. ×
Activity Recognition from User-Annotated Acceleration Data Ling ...
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×
Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

Activity Recognition from User-Annotated Acceleration Data Ling ...

608
views

Published on


0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
608
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
16
Comments
0
Likes
0
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
No notes for slide

Transcript

  • 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
    • 4. Lab environments may restrict activity, simplifying recognition !
    • 5. 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)
  • 6. Data Collection
    What’s an accelerometer ?
    An accelerometer is a device that measures the vibration, or acceleration of motion of a structure.
  • 7. 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
  • 8. Sensor Placement
  • Raw Data
  • 15. 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
    ?
  • 16. 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
  • 17. Classifiers
    Tested on decision table, nearest neighbor ( IBL), C4.5 decision tree, and naïve Bayesian classifiers
    Machine Learning Toolkit (Witten & Frank, 1999)
  • 18. 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
  • 19. 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
  • 20. 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)
  • 21. 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
  • 22. 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%)
  • 23. 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)
  • 24. 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
  • 25. Thank you!
    The End
    For some slides, I used content of Emmanuel MunguiaTapia’s presentation

×