eRum presentation on Tools for working with TensorFlow in R
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README.md

eRum 2018: Tools for working with TensorFlow in R

About the data

Activity Recognition from Single Chest-Mounted Accelerometer Data Set

Source data

Source:

Uncalibrated Accelerometer Data are collected from 15 participantes performing 7 activities. The dataset provides challenges for identification and authentication of people using motion patterns.

Data Set Information:

  • The dataset collects data from a wearable accelerometer mounted on the chest
  • Sampling frequency of the accelerometer: 52 Hz
  • Accelerometer Data are Uncalibrated
  • Number of Participants: 15
  • Number of Activities: 7
  • Data Format: CSV

Attribute Information:

  • Data are separated by participant
  • Each file contains the following information *- sequential number, x acceleration, y acceleration, z acceleration, label
  • Labels are codified by numbers
  • 1: Working at Computer
  • 2: Standing Up, Walking and Going updown stairs
  • 3: Standing
  • 4: Walking
  • 5: Going UpDown Stairs
  • 6: Walking and Talking with Someone
  • 7: Talking while Standing

Data preparation

The data was pre-processed by Mango Solutions for their keras workshop at eRum 2018:

  • The raw data is in long format with a running label on person ID and activity ID.
  • We broke it into overlapping chunks and filtered for walking-only chunks of 5 seconds.
  • We did some center-scaling but only within chunks
library(ggplot2)
library(tidyr)
x_walk <- readRDS("data/x_walk.rds")
x_walk$train[1, 1:5, ]
#>           acc_x      acc_y       acc_z
#> [1,] -0.3195554 -0.7566914 -0.03613472
#> [2,] -0.4358387 -0.3765424 -0.06066481
#> [3,] -0.2904846  0.0669647  0.08651576
#> [4,] -0.9591135  0.3964272 -0.55126669
#> [5,] -1.7149549  0.5991733 -0.67391716
frame <- as.data.frame(x_walk$train[1, , ])
frame$time <- 1:260
tidy_frame <- gather(frame, acc, value, -time)
str(tidy_frame)
#> 'data.frame':    780 obs. of  3 variables:
#>  $ time : int  1 2 3 4 5 6 7 8 9 10 ...
#>  $ acc  : chr  "acc_x" "acc_x" "acc_x" "acc_x" ...
#>  $ value: num  -0.32 -0.436 -0.29 -0.959 -1.715 ...
ggplot(tidy_frame, aes(time, value)) + geom_line() + facet_grid(~acc)