Activity Recognition from User-Annotated Acceleration Data Ling Bao and Stephen S. Intille<br />Presented by: Hong Lu<br />
Key Questions<br />Can low cost wearable sensors be used for robust, real- time recognition of activity? <br />Can trainin...
Data Collection<br />13 ♂ + 7♀ = 20 subjects , age from 17 to 48 <br />20 everyday activities <br />Subjects unsupervised ...
Lab environments may restrict activity, simplifying recognition  !
Making researchers to label training examples does not scale</li></ul>Recognition rates highly depended on how data is col...
Data Collection<br />What’s an accelerometer ?<br /> An accelerometer is a device that measures the vibration, or accelera...
Why Accelerometer ?<br />Many daily activities involve repetitive physical motion of the body or specific postures<br /> E...
Sensor Placement <br /><ul><li>5 wireless sensors
Right hip
Wrist
upper arm
Ankle
Thigh
Shack to synchronize</li></li></ul><li>Raw Data <br />
Features<br />Why we need them ?<br />Summarize the data bin<br />Capture useful information <br />What is the desired cha...
Features<br />512 sample windows (6.7s ?), 50% window overlap <br />Features: <br />Mean <br />Energy <br />Frequency-doma...
Classifiers<br />Tested on decision table, nearest neighbor ( IBL), C4.5 decision tree, and naïve Bayesian classifiers<br ...
Training<br />Method 1: User-specific training <br />Train on activity sequence data for each subject <br />Test on obstac...
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Activity Recognition from User-Annotated Acceleration Data Ling ...

  1. 1. Activity Recognition from User-Annotated Acceleration Data Ling Bao and Stephen S. Intille<br />Presented by: Hong Lu<br />
  2. 2. Key Questions<br />Can low cost wearable sensors be used for robust, real- time recognition of activity? <br />Can training data be acquired from the end user without researcher supervision? <br />Does recognition require user-specific training data? <br />Do more sensors improve recognition? <br />
  3. 3. Data Collection<br />13 ♂ + 7♀ = 20 subjects , age from 17 to 48 <br />20 everyday activities <br />Subjects unsupervised when generating own training data, both in and outside the lab<br />What’s the problem of typical laboratory data? WHY?<br /><ul><li>Often data in lab is collected from researchers as subjects
  4. 4. Lab environments may restrict activity, simplifying recognition !
  5. 5. Making researchers to label training examples does not scale</li></ul>Recognition rates highly depended on how data is collected<br /> 95.6% (laboratory data) <br /> VS<br /> 66.7% (naturalistic settings)<br />
  6. 6. Data Collection<br />What’s an accelerometer ?<br /> An accelerometer is a device that measures the vibration, or acceleration of motion of a structure. <br />
  7. 7. Why Accelerometer ?<br />Many daily activities involve repetitive physical motion of the body or specific postures<br /> E.g. Walking, Running, Scrubbing, Vacuuming<br />Low cost, tiny, energy efficient <br />Watch<br />Phone, mp3 player<br />Camera<br />computer<br />Game controller, the wii remote <br />
  8. 8. Sensor Placement <br /><ul><li>5 wireless sensors
  9. 9. Right hip
  10. 10. Wrist
  11. 11. upper arm
  12. 12. Ankle
  13. 13. Thigh
  14. 14. Shack to synchronize</li></li></ul><li>Raw Data <br />
  15. 15. Features<br />Why we need them ?<br />Summarize the data bin<br />Capture useful information <br />What is the desired characteristics of a good feature ?<br />removing irrelevant noise<br />keeping relevant attributes to tell the difference<br />easy to compute<br />?<br />
  16. 16. Features<br />512 sample windows (6.7s ?), 50% window overlap <br />Features: <br />Mean <br />Energy <br />Frequency-domain entropy <br />Correlation Between x, y accelerometer axes each board Between all pair wise combinations of axes on different boards<br />
  17. 17. Classifiers<br />Tested on decision table, nearest neighbor ( IBL), C4.5 decision tree, and naïve Bayesian classifiers<br /> Machine Learning Toolkit (Witten & Frank, 1999)<br />
  18. 18. Training<br />Method 1: User-specific training <br />Train on activity sequence data for each subject <br />Test on obstacle course data for that subject <br />Method 2: Leave-one-subject out training<br />Train on activity sequence and activity data for all subjects but one <br />Test on obstacle course data for left out subject<br />Average for all 20 subjects <br />
  19. 19. Results<br />C45 Decision tree wins<br />It shows<br />User-specific training: 71.6 ±7.4 <br />Leave-one-subject-out training: 84.3 ±5.2 <br />Why? <br />Commonalities between people may be more significant than individual variations <br />Larger training set<br />
  20. 20. Result<br />Overall, promising <br />Data collected by subjects themselves without supervision <br />Data collected both in and outside of laboratory setting <br />Poorer performance results when… <br />Activities involve less physically characteristic movements , Activities involve little motion or standing still <br />Activities involve similar posture/movement (e.g. watching TV, sitting and relaxing)<br />
  21. 21. The dark side <br />The more sensors you placed, the higher accuracy you may achieved, but …<br />cost<br />you look weird<br />hard to deploy<br />more computational horse power <br />
  22. 22. Accelerometer Discriminatory Power<br />Tested C4.5 classifier with using subsets of accelerometers: <br />Hip, wrist, arm, ankle, thigh, thigh and wrist, hip and wrist <br />Best single performers: <br />Thigh (-29.5%) <br />Hip (-34.1%) <br />Ankle(-37%)<br />
  23. 23. Accelerometer Discriminatory Power<br />With only two accelerometers get good performance: <br />Thigh and wrist (-3.3% compared with all 5) <br />Hip and wrist (-4.8% compared with all 5) <br />
  24. 24. Overview<br />The study <br />Activity recognition: 20 household activities<br />Sensors: 5 non-wired accelerometers <br />Data: participants labeled own data<br />Result <br />Good performance with decision tree classifier<br />Subject-specific training data for some activities may not be required <br />Reasonable accuracy can be achieved with only 2 of 5 accelerometers<br />
  25. 25. Thank you!<br />The End<br />For some slides, I used content of Emmanuel MunguiaTapia’s presentation <br />

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