Activity Recognition from UserAnnotated Acceleration Data
Presented by James Reinebold
CSCI 546
Outline
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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
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Walking
Sitting and Relaxing
Standing Still
Watching TV
Running
Stretching
Scrubbing
Folding Laundry
Brushing Teeth
Riding Elevator
Walking Carrying Items
Working on Computer
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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
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
Weight
>= 20 pounds
< 20 pounds
Cat
Friendliness
Friendly
Dog
Not friendly
Goat
Results
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
• Decision tree was the best performer, but…
Aggregate Activity Recognition Rates
Riding Escalator
Climbing Stairs
Lying down & relaxing
Vacuuming
Strength-training
Bicycling
Reading
Eating or drinking
Working on computer
Walking carrying items
Riding Elevator
Brushing Teeth
Folding Laundry
Scrubbing
Stretching
Running
Watching TV
Standing still
Sitting & relaxing
Walking
0
20
40
60
80
100
120
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
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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/naiveBay
esianClassifier.pdf