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Activity Recognition from User-Annotated Acceleration Data

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24 diapositives · 387 Views · Oct 21, 2014

This presentation explores the feasibility of recognizing human activities using mobile sensor data, particularly accelerometers. The study design emphasizes user-driven data collection in semi-naturalistic settings, promoting ecological validity. Various classification methods, including decision trees and nearest neighbor algorithms, were evaluated to distinguish daily activities such as walking, sitting, and running. Results indicate that accelerometers effectively capture activity patterns, with insights on sensor placement and algorithm performance. The critique highlights both the strengths and practical limitations of the approach, paving the way for future research.

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  1. Activity Recognition from User-Annotated Acceleration Data Presented by James Reinebold CSCI 546

  2. Outline • Motivation • Experiment Design • Classification Methods Used • Results • Conclusion • Critique

  3. Motivation • Can we recognize human activities based on mobile sensor data? • Applications • Medicine • Fitness • Security

  4. Related Work • Recognition of gait pace and incline [Aminan, et. al. 1995] • Sedentary vs. vigorous activities [Welk and Differding 2000] • Unsupervised learning [Krause, et. al. 2003]

  5. Scientifically Meaningful Data • Most research is done in highly controlled experiments. • Occasionally the test subjects are the researchers themselves! • Can we generalize to the real world? • Noisy • Inconsistent • Sensors must be practical • We need ecologically valid results.

  6. Experiment Design • Semi-Naturalistic, User-Driven Data Collection • Obstacle course / worksheet • No researcher supervision while subjects performed the tasks • Timer synchronization • Discard data within 10 seconds of start and finish time for activities

  7. Experiment Design (2) Source: Bao 2004

  8. Sensors Used • Five ADXL210E accelerometers (manufactured by Analog Devices) • Range of +/- 10g • 5mm x 5mm x 2mm • Low Power, Low Cost • Measures both static and dynamic acceleration • “Hoarder Board” Source: http://vadim.oversigma.com/Hoarder/LayoutFront.htm

  9. Activities • Walking • Sitting and Relaxing • Standing Still • Watching TV • Running • Stretching • Scrubbing • Folding Laundry • Brushing Teeth • Riding Elevator • Walking Carrying Items • Working on Computer • Eating or Drinking • Reading • Bicycling • Strength-training • Vacuuming • Lying down & relaxing • Climbing stairs • Riding escalator

  10. Example Signals Source: Bao 2004

  11. Activity Recognition Algorithm • FFT-based feature computation • Sample at 76.25 Hz • 512 sample windows • Extract mean energy, entropy, and correlation features • Classifier algorithms • All supervised learning techniques

  12. Source: Bao 2004

  13. Naïve Bayes Classifier • Multiplies the probability of an observed datapoint by looking at the priority probabilities that encompass the training set. • P(B|A) = P(A|B) * P(B) / P(A) • Assumes that each of the features are independent. • Relatively fast. Source: cis.poly.edu/~mleung/FRE7851/f07/naiveBayesianClassifier.pdf

  14. Nearest Neighbor • Split up the domain into various dimensions, with each dimension corresponding to a feature. • Classify an unknown point by having its K nearest neighbors “vote” on who it belongs to. • Simple, easy to implement algorithm. Does not work well when there are no clusters. Source: http://pages.cs.wisc.edu/~dyer/cs540/notes/learning.html

  15. Nearest Neighbor Example

  16. Decision Trees • Make a tree where the non-leaf nodes are the features, and each leaf node is a classification. Each edge of the tree represents a value range of the feature. • Move through the tree until you arrive at a leaf node • Generally, the smaller the tree the better. • Finding the smallest is NP-Hard Source: http://pages.cs.wisc.edu/~dyer/cs540/notes/learning.html

  17. Decision Tree Example Weight >= 20 pounds < 20 pounds Cat Friendliness Friendly Not friendly Dog Goat

  18. Results • Decision tree was the best performer, but…

  19. Trying With Less Sensors

  20. Conclusion • Accelerometers can be used to affectively distinguish between everyday activities. • Decision trees and nearest neighbor algorithms are good choices for activity recognition. • Some sensor locations are more important than others.

  21. Critique - Strengths • Ecological validity • Devices cannot just work in the lab, they have to live in the real world. • Variety of classifiers used • Decent sample size

  22. Critique - Weaknesses • Lack of supervision • Practicality of wearing five sensors • Post-processing? • Why only accelerometers? • Heart rate • Respiration rate • Skin conductance • Microphone • Etc..

  23. Sources • www.analog.com • http://vadim.oversigma.com/Hoarder/Hoarder.htm • http://pages.cs.wisc.edu/~dyer/cs540/notes/learning.html • cis.poly.edu/~mleung/FRE7851/f07/naiveBayesianClassifier.pdf