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