Activity Recognition from User-Annotated Acceleration Data Presented by James Reinebold CSCI 546
Outline • Motivation • Experiment Design • Classification Methods Used • Results • Conclusion • Critique
Motivation • Can we recognize human activities based on mobile sensor data? • Applications • Medicine • Fitness • Security
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]
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.
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
Experiment Design (2) Source: Bao 2004
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
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
Example Signals Source: Bao 2004
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
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
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
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
Decision Tree Example Weight >= 20 pounds < 20 pounds Cat Friendliness Friendly Not friendly Dog Goat
Results • Decision tree was the best performer, but…
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.
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
Critique - Weaknesses • Lack of supervision • Practicality of wearing five sensors • Post-processing? • Why only accelerometers? • Heart rate • Respiration rate • Skin conductance • Microphone • Etc..
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