Activity Recognition from User-Annotated Acceleration Data
Presented by James ReineboldCSCI 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
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
Source: Bao 2004
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
Nearest Neighbor Example
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
WeightWeight
FriendlinessFriendliness
DogDogGoatGoat
CatCat
< 20 pounds>= 20 pounds
Not friendlyFriendly
Results
• Decision tree was the best performer, but…
Classifier User-specific Training Leave-one-subject-out Training
Decision Table 36.32 +/- 14.501 46.75 +/- 9.296
Nearest Neighbor 69.21 +/- 6.822 82.70 +/- 6.416
Decision Tree 71.58 +/- 7.438 84.26 +/- 5.178
Naïve Bayes 34.94 +/- 5.818 52.35 +/- 1.690
Trying With Less Sensors
Accelerometer (s) Left In Difference in Recognition Activity
Hip -34.12 +/- 7.115
Wrist -51.99 +/- 12.194
Arm -63.65 +/- 13.143
Ankle -37.08 +/- 7.601
Thigh -29.47 +/- 4.855
Thigh and Wrist -3.27 +/- 1.062
Hip and Wrist -4.78 +/- 1.331
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/Hoarde
r.htm• http://pages.cs.wisc.edu/~dyer/cs540/notes/l
earning.html• cis.poly.edu/~mleung/FRE7851/f07/naiveBayesianClassifier.pdf
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