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Spring Semester, 2010

Dynamic Time Warping and Neural Network. 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.

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Spring Semester, 2010

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  1. Dynamic Time Warping and Neural Network J.-Y. Yang, J.-S. Wang and Y.-P. Chena,Using acceleration measurements for activity recognition: An effective learning algorithm for constructing neural classifiersPattern Recognition Letters, vol. 29, no. 16, pp. 2213-2220, 2008. Spring Semester, 2010

  2. Outline • Background • Activity Recognition Strategy • Experiments • Summary

  3. 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

  4. Background 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

  5. Background 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)

  6. Background 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

  7. Background Artificial Neurons y g( ) ∑w.x w1 w3 w2 x3 x1 x2

  8. Background 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

  9. Activity Recognition Strategy • Pre-Classifier • Static/Dynamic Classifier

  10. Activity Recognition Strategy 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

  11. Activity Recognition Strategy 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

  12. Activity Recognition Strategy Feature Extraction • 8 attributes × 3axis = 24 features • Mean, correlation between axes, energy, interquartile range (IQR), mean absolute deviation, root mean square, standard deviation, variance

  13. Activity Recognition Strategy Feature Selection (1/2) • Common principalcomponent analysis (CPCA) • If features are highlycorrelated,the corresponding vectorsare similar clustering to group similar loadings

  14. Activity Recognition Strategy Feature Selection (2/2) • Apply the PCA • Select the first p PCs (cumulative sum>90%) • Estimate CPC • Support vector clustering

  15. Activity Recognition Strategy Verification

  16. 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

  17. Experiments 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

  18. Experiments FSS Evaluation • Use six static selected features

  19. Experiments 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

  20. 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)

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