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CS 2750 Project Report. Jason D. Bakos. Project Goals. Data Sensor readings from 11 different people walking in a controlled environment An accelerometer records floor vibration data from footfalls A microphone records sounds from footballs This data is recorded 10 times for each person

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project goals
Project Goals
  • Data
    • Sensor readings from 11 different people walking in a controlled environment
    • An accelerometer records floor vibration data from footfalls
    • A microphone records sounds from footballs
    • This data is recorded 10 times for each person
      • Data gathered from 11 different people
project goals3
Project Goals
  • Use this data to perform multiple classification
    • Human gait analysis
  • Eventually want to determine if a person is in duress
  • Most important aspect: learn the nature of the data to determine how best to classify it
data preprocessing
Data Preprocessing
  • Data size
    • Data is collected at 15KHz for approximately 10 seconds
    • 150,000 samples
  • Must get data out of time domain
    • Must capture a “walk” as a single data point
    • Time series => cross sectional
data preprocessing5
Data Preprocessing
  • Extract the largest intensity step from the data
    • Closest to sensors
  • Transform data to frequency domain
    • Fourier transform
      • Used MatLab FFT – output is real array
    • Integrated over time
  • Bin resultant data into bins
    • These are now the features
data preprocessing6
Data Preprocessing
  • Extracting footstep
    • Method 1
      • Find max value in time-domain
      • Center fixed window around data
      • 2000, 4000, 6000
    • Method 2
      • Actively find footstep
      • Create new vector by recording sliding abs “mean”-window
      • Extract largest hill (using gradient descent and threshold)
      • Index from meanarray into data array
      • Meanwindow sizes 1000, 2000, 3000
data preprocessing7
Data Preprocessing

Mean window of 1000

data preprocessing8
Data Preprocessing

Mean window of 2000

data preprocessing9
Data Preprocessing

Mean window of 3000

analysis of preprocessed data
Analysis of Preprocessed Data
  • Cluster analysis
    • Unsupervised learning
    • 3 steps
      • Distance calculation
      • Linkage analysis
      • Clustering
analysis of preprocessed data11
Analysis of Preprocessed Data
  • Distance Calculation
    • 4 distance measures
      • Euclid
        • Standard distance
      • Standardized Euclid
        • Shorter distance between points who have relatively smaller variances
      • City Block
        • Similar to Euclid, used for comparison
      • Minkowski
        • Another way to measure distance, used for comparison
      • Result is array, distance from each point to every other point
analysis of preprocessed data12
Analysis of Preprocessed Data
  • Linkage Analysis
    • Hierarchically link datapoints
    • Methods
      • Shortest distance
      • Average distance
        • Uses center points of clusters
      • Centroid distance
        • Draws “sphere” around center point, uses furthest point as radius – use distance from edges of sphere
      • Incremental sum-of-squares
        • Similar to centroid, used for comparison
    • Result is matrix
analysis of preprocessed data13
Analysis of Preprocessed Data
  • Clustering
    • Force datapoints into a fixed number of clusters
    • Result is cluster vector and dendrogram
analysis of preprocessed data14
Analysis of Preprocessed Data
  • How to judge how well the clustering worked?
  • My answer
    • Since there is exactly 10 samples from 11 people, define “uniformity” as a metric
analysis of preprocessed data16
Analysis of Preprocessed Data
  • Checked all 12 charts
    • fix2000, fix4000, fix6000, win1000, win2000, win3000 for vibration and audio
    • Euclid/Sum-of-squares is best for vibration and audio
    • win3000 is best for vibration
    • fix2000 is best for audio
indirect learning
Indirect Learning
  • Used parametric Naïve Bayes model to do multi-way classification
    • 11 classes
  • Used 50-bin data
  • Assumed data was multivariate Gaussian
  • Chose class based on maxium posterior of C
  • Used multiple train/test splits to train 3 models with bagging (voting)
indirect learning20
Indirect Learning
  • Bad results
    • Worse than random predictor
  • Conclusion
    • Data is not Gaussian
direct learning
Direct Learning
  • Trained neural network with same data
  • Used softmax network to perform multiway classification
  • 1000 epochs, log sigmoid, gradient descent
  • Tried different parameters for neural network
direct learning22
Direct Learning

Vibration

Audio

direct learning23
Direct Learning
  • No improvement after 50 neurons per level (vib and aud)
  • 4 levels is best (including output level)
  • Results terrible for test sets
conclusion
Conclusion
  • Need
    • Better feature extraction
    • Better classifiers
  • Or… maybe different sensors are needed
    • Video