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 Data gathered from 11 different people

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 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 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 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 Preprocessing

Mean window of 1000

Data Preprocessing

Mean window of 2000

Data Preprocessing

Mean window of 3000

Analysis of Preprocessed Data Cluster analysis

Unsupervised learning 3 steps

Distance calculation Linkage analysis Clustering

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 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 Data Clustering

Force datapoints into a fixed number of clusters

Result is cluster vector and dendrogram

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

( ) 1, 1 11j cluster i

count i for i

11 11

1 1

1

( ) ( )i j

uniformitycount i count j

Analysis of Preprocessed Data

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

Analysis of Preprocessed Data

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 Learning

Indirect Learning Bad results

Worse than random predictor Conclusion

Data is not Gaussian

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 Learning

Vibration

Audio

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 Need

Better feature extraction Better classifiers

Or… maybe different sensors are needed Video