activity recognition from user-annotated acceleration data
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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 - PowerPoint PPT PresentationTRANSCRIPT
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