J.-Y. Yang, J.-S. Wang and Y.-P. Chena,

Using acceleration measurements for activity recognition: An effective learning algorithm for constructing neural classifiers Pattern Recognition Letters, vol. 29, no. 16, pp. 2213-2220, 2008.

Spring Semester, 2010

Dynamic Time Warping and Neural Network

Outline

Background Activity Recognition Strategy Experiments Summary

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Background

Accelerometers can be used as a human motion detector and monitoring device– Biomedical engineering, medical nursing, interactive

entertainment, …

– Exercise intensity / distance, sleep cycle, and calorie consumption

Proposed Method Overview

One 3-D accelerometer on the dominant wrist

NNs– Pre-classifier static classifier or dynamic classifier

Eight domestic activities– Standing, sitting, walking, running, vacuuming, scrubbing,

brushing teeth, and working at a computer

Background

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Neural Classifier

Neurons in the Brain– A neuron receives input from other neurons (generally

thousands) from its synapses

– Inputs are approximately summed

– When the input exceeds a threshold the neuron sends an electrical spike that travels from the body, down the axon, to the next neuron(s)

Background

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Neurons in the Brain (cont.)

Amount of signal passing through a neuron depends on:– Intensity of signal from feeding neurons

– Their synaptic strengths

– Threshold of the receiving neuron

Hebb rule (plays key part in learning) – A synapse which repeatedly triggers the activation of a

postsynaptic neuron will grow in strength, others will gradually weaken

– Learn by adjusting magnitudes of synapses’ strengths

Background

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Artificial Neurons

w1w2

w3

x1

x2

x3

y

∑w.x

g( )A

f(A)

+1

-1

0

Step Function

A

f(A)

+1

-1

0

Sigmoid Function

Background

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Neural Classifier (Perceptron)

Structure

Learning– Weights are changed in proportion to the difference (error)

between target output and perceptron solution for each example

– Back-propagation algorithm• The gradient descent method, Slow convergence and local minima

– The resilient back-propagation (RPROP)• Ignore the magnitude of the gradient

Background

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Activity Recognition Strategy

Pre-Classifier

Static/Dynamic Classifier

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Pre-Classifier (1/2)

Two components of the acceleration data– Gravitational acceleration (GA)

– Body acceleration (BA): High-pass filtering to remove GA

Segmentation with overlapping windows– 512 samples per window

Activity Recognition Strategy

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Pre-Classifier (2/2)

SMA (Signal Magnitude Area)– The sum of acceleration magnitude over three axes

AE (Average Energy)– Average of the energy over three axes

– Energy: The sum of the squared discrete FFT component magnitudes of the signal in a window

Activity Recognition Strategy

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Feature Extraction

8 attributes × 3axis = 24 features– Mean, correlation between axes, energy, interquartile range

(IQR), mean absolute deviation, root mean square, standard deviation, variance

Activity Recognition Strategy

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Feature Selection (1/2)

Common principalcomponent analysis (CPCA)

If features are highlycorrelated,the corresponding vectorsare similar clustering to group similar loadings

Activity Recognition Strategy

Feature Selection (2/2)

Apply the PCA Select the first p PCs (cumulative sum>90%) Estimate CPC Support vector clustering

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Activity Recognition Strategy

Verification

Activity Recognition Strategy

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Experiments: Environment (1/2)

MMA7260Q tri-axial accelerometer– Sensitivity: -4.0g ~ +4.0g, 100Hz

– Mount on the dominant wrist

Eight activities from seven subjects– Standing, sitting, walking,

running, vacuuming,scrubbing, brushing teeth,and working at a computer

– 2min per activity

Environment (2/2)

Window size = 512 (with 256 overlapping)– 22 windows in one min., 45 windows in two min.

Leave-one-subject-out cross-validation– Training: 1min per activity = 22 windows × 8 activities× 6

subjects

– Test: 2min per activity = 45 windows × 8 activities

Experiments

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FSS Evaluation

Use six static selected features

Experiments

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Recognition Result

NN– Hidden node

• Pre-classifier: 3

• Static-classifier: 5

• Dynamic-classifier: 7

– Epochs: 500

Computational load of FSS– Training without FSS = 7.457s, training with FSS = 8.46s

Experiments

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Summary

Proposed method yielded 95% accuracy– Pre-classifier static / dynamic classifiers

Author’s other publication– Yen-Ping Chen, Jhun-Ying Yang, Shun-Nan Liou, Gwo-Yun Lee, Jeen-Shing Wang: Online

classifier construction algorithm for human activity detection using a tri-axial accelerometer.

– Applied Mathematics and Computation 205(2): 849-860 (2008)