Classifying bodily exercise from smartphone information

Introduction

On this submit we’ll describe learn how to use smartphone accelerometer and gyroscope information to foretell the bodily actions of the people carrying the telephones. The information used on this submit comes from the Smartphone-Primarily based Recognition of Human Actions and Postural Transitions Knowledge Set distributed by the College of California, Irvine. Thirty people have been tasked with performing numerous primary actions with an hooked up smartphone recording motion utilizing an accelerometer and gyroscope.

Earlier than we start, let’s load the varied libraries that we’ll use within the evaluation:

library(keras)     # Neural Networks
library(tidyverse) # Knowledge cleansing / Visualization
library(knitr)     # Desk printing
library(rmarkdown) # Misc. output utilities 
library(ggridges)  # Visualization

Actions dataset

The information used on this submit come from the Smartphone-Primarily based Recognition of Human Actions and Postural Transitions Knowledge Set(Reyes-Ortiz et al. 2016) distributed by the College of California, Irvine.

When downloaded from the hyperlink above, the information comprises two totally different ‘elements.’ One which has been pre-processed utilizing numerous characteristic extraction strategies equivalent to fast-fourier remodel, and one other RawData part that merely provides the uncooked X,Y,Z instructions of an accelerometer and gyroscope. None of the usual noise filtering or characteristic extraction utilized in accelerometer information has been utilized. That is the information set we are going to use.

The motivation for working with the uncooked information on this submit is to assist the transition of the code/ideas to time collection information in much less well-characterized domains. Whereas a extra correct mannequin could possibly be made by using the filtered/cleaned information supplied, the filtering and transformation can fluctuate drastically from activity to activity; requiring a number of handbook effort and area information. One of many stunning issues about deep studying is the characteristic extraction is discovered from the information, not exterior information.

Exercise labels

The information has integer encodings for the actions which, whereas not essential to the mannequin itself, are useful to be used to see. Let’s load them first.

activityLabels <- learn.desk("information/activity_labels.txt", 
                             col.names = c("quantity", "label")) 

activityLabels %>% kable(align = c("c", "l"))

Subsequent, we load within the labels key for the RawData. This file is a listing of the entire observations, or particular person exercise recordings, contained within the information set. The important thing for the columns is taken from the information README.txt.

Column 1: experiment quantity ID, 
Column 2: consumer quantity ID, 
Column 3: exercise quantity ID 
Column 4: Label begin level 
Column 5: Label finish level 

The beginning and finish factors are in variety of sign log samples (recorded at 50hz).

Let’s check out the primary 50 rows:

labels <- learn.desk(
  "information/RawData/labels.txt",
  col.names = c("experiment", "userId", "exercise", "startPos", "endPos")
)

labels %>% 
  head(50) %>% 
  paged_table()

File names

Subsequent, let’s take a look at the precise information of the consumer information supplied to us in RawData/

dataFiles <- record.information("information/RawData")
dataFiles %>% head()

[1] "acc_exp01_user01.txt" "acc_exp02_user01.txt"
[3] "acc_exp03_user02.txt" "acc_exp04_user02.txt"
[5] "acc_exp05_user03.txt" "acc_exp06_user03.txt"

There’s a three-part file naming scheme. The primary half is the kind of information the file comprises: both acc for accelerometer or gyro for gyroscope. Subsequent is the experiment quantity, and final is the consumer Id for the recording. Let’s load these right into a dataframe for ease of use later.

fileInfo <- data_frame(
  filePath = dataFiles
) %>%
  filter(filePath != "labels.txt") %>% 
  separate(filePath, sep = '_', 
           into = c("kind", "experiment", "userId"), 
           take away = FALSE) %>% 
  mutate(
    experiment = str_remove(experiment, "exp"),
    userId = str_remove_all(userId, "consumer|.txt")
  ) %>% 
  unfold(kind, filePath)

fileInfo %>% head() %>% kable()

Studying and gathering information

Earlier than we will do something with the information supplied we have to get it right into a model-friendly format. This implies we wish to have a listing of observations, their class (or exercise label), and the information similar to the recording.

To acquire this we are going to scan by every of the recording information current in dataFiles, lookup what observations are contained within the recording, extract these recordings and return all the things to a simple to mannequin with dataframe.

# Learn contents of single file to a dataframe with accelerometer and gyro information.
readInData <- operate(experiment, userId){
  genFilePath = operate(kind) {
    paste0("information/RawData/", kind, "_exp",experiment, "_user", userId, ".txt")
  }  
  
  bind_cols(
    learn.desk(genFilePath("acc"), col.names = c("a_x", "a_y", "a_z")),
    learn.desk(genFilePath("gyro"), col.names = c("g_x", "g_y", "g_z"))
  )
}

# Perform to learn a given file and get the observations contained alongside
# with their lessons.

loadFileData <- operate(curExperiment, curUserId) {
  
  # load sensor information from file into dataframe
  allData <- readInData(curExperiment, curUserId)

  extractObservation <- operate(startPos, endPos){
    allData[startPos:endPos,]
  }
  
  # get commentary areas on this file from labels dataframe
  dataLabels <- labels %>% 
    filter(userId == as.integer(curUserId), 
           experiment == as.integer(curExperiment))
  

  # extract observations as dataframes and save as a column in dataframe.
  dataLabels %>% 
    mutate(
      information = map2(startPos, endPos, extractObservation)
    ) %>% 
    choose(-startPos, -endPos)
}

# scan by all experiment and userId combos and collect information right into a dataframe. 
allObservations <- map2_df(fileInfo$experiment, fileInfo$userId, loadFileData) %>% 
  right_join(activityLabels, by = c("exercise" = "quantity")) %>% 
  rename(activityName = label)

# cache work. 
write_rds(allObservations, "allObservations.rds")
allObservations %>% dim()

Exploring the information

Now that we now have all the information loaded together with the experiment, userId, and exercise labels, we will discover the information set.

Size of recordings

Let’s first take a look at the size of the recordings by exercise.

allObservations %>% 
  mutate(recording_length = map_int(information,nrow)) %>% 
  ggplot(aes(x = recording_length, y = activityName)) +
  geom_density_ridges(alpha = 0.8)

The very fact there’s such a distinction in size of recording between the totally different exercise varieties requires us to be a bit cautious with how we proceed. If we practice the mannequin on each class directly we’re going to should pad all of the observations to the size of the longest, which would go away a big majority of the observations with an enormous proportion of their information being simply padding-zeros. Due to this, we are going to match our mannequin to simply the most important ‘group’ of observations size actions, these embody STAND_TO_SIT, STAND_TO_LIE, SIT_TO_STAND, SIT_TO_LIE, LIE_TO_STAND, and LIE_TO_SIT.

An fascinating future course can be making an attempt to make use of one other structure equivalent to an RNN that may deal with variable size inputs and coaching it on all the information. Nonetheless, you’ll run the danger of the mannequin studying merely that if the commentary is lengthy it’s most certainly one of many 4 longest lessons which might not generalize to a state of affairs the place you have been operating this mannequin on a real-time-stream of information.

Filtering actions

Primarily based on our work from above, let’s subset the information to simply be of the actions of curiosity.

desiredActivities <- c(
  "STAND_TO_SIT", "SIT_TO_STAND", "SIT_TO_LIE", 
  "LIE_TO_SIT", "STAND_TO_LIE", "LIE_TO_STAND"  
)

filteredObservations <- allObservations %>% 
  filter(activityName %in% desiredActivities) %>% 
  mutate(observationId = 1:n())

filteredObservations %>% paged_table()

So after our aggressive pruning of the information we could have a good quantity of information left upon which our mannequin can study.

Coaching/testing cut up

Earlier than we go any additional into exploring the information for our mannequin, in an try and be as truthful as attainable with our efficiency measures, we have to cut up the information right into a practice and take a look at set. Since every consumer carried out all actions simply as soon as (excluding one who solely did 10 of the 12 actions) by splitting on userId we are going to be certain that our mannequin sees new individuals completely after we take a look at it.

# get all customers
userIds <- allObservations$userId %>% distinctive()

# randomly select 24 (80% of 30 people) for coaching
set.seed(42) # seed for reproducibility
trainIds <- pattern(userIds, measurement = 24)

# set the remainder of the customers to the testing set
testIds <- setdiff(userIds,trainIds)

# filter information. 
trainData <- filteredObservations %>% 
  filter(userId %in% trainIds)

testData <- filteredObservations %>% 
  filter(userId %in% testIds)

Visualizing actions

Now that we now have trimmed our information by eradicating actions and splitting off a take a look at set, we will really visualize the information for every class to see if there’s any instantly discernible form that our mannequin could possibly decide up on.

First let’s unpack our information from its dataframe of one-row-per-observation to a tidy model of all of the observations.

unpackedObs <- 1:nrow(trainData) %>% 
  map_df(operate(rowNum){
    dataRow <- trainData[rowNum, ]
    dataRow$information[[1]] %>% 
      mutate(
        activityName = dataRow$activityName, 
        observationId = dataRow$observationId,
        time = 1:n() )
  }) %>% 
  collect(studying, worth, -time, -activityName, -observationId) %>% 
  separate(studying, into = c("kind", "course"), sep = "_") %>% 
  mutate(kind = ifelse(kind == "a", "acceleration", "gyro"))

Now we now have an unpacked set of our observations, let’s visualize them!

unpackedObs %>% 
  ggplot(aes(x = time, y = worth, shade = course)) +
  geom_line(alpha = 0.2) +
  geom_smooth(se = FALSE, alpha = 0.7, measurement = 0.5) +
  facet_grid(kind ~ activityName, scales = "free_y") +
  theme_minimal() +
  theme( axis.textual content.x = element_blank() )

So at the least within the accelerometer information patterns undoubtedly emerge. One would think about that the mannequin could have bother with the variations between LIE_TO_SIT and LIE_TO_STAND as they’ve an analogous profile on common. The identical goes for SIT_TO_STAND and STAND_TO_SIT.

Preprocessing

Earlier than we will practice the neural community, we have to take a few steps to preprocess the information.

Padding observations

First we are going to determine what size to pad (and truncate) our sequences to by discovering what the 98th percentile size is. By not utilizing the very longest commentary size this may assist us keep away from extra-long outlier recordings messing up the padding.

padSize <- trainData$information %>% 
  map_int(nrow) %>% 
  quantile(p = 0.98) %>% 
  ceiling()
padSize

98% 
334 

Now we merely must convert our record of observations to matrices, then use the tremendous helpful pad_sequences() operate in Keras to pad all observations and switch them right into a 3D tensor for us.

convertToTensor <- . %>% 
  map(as.matrix) %>% 
  pad_sequences(maxlen = padSize)

trainObs <- trainData$information %>% convertToTensor()
testObs <- testData$information %>% convertToTensor()
  
dim(trainObs)

[1] 286 334   6

Great, we now have our information in a pleasant neural-network-friendly format of a 3D tensor with dimensions (, , ).

One-hot encoding

There’s one last item we have to do earlier than we will practice our mannequin, and that’s flip our commentary lessons from integers into one-hot, or dummy encoded, vectors. Fortunately, once more Keras has equipped us with a really useful operate to just do this.

oneHotClasses <- . %>% 
  {. - 7} %>%        # deliver integers right down to 0-6 from 7-12
  to_categorical() # One-hot encode

trainY <- trainData$exercise %>% oneHotClasses()
testY <- testData$exercise %>% oneHotClasses()

Modeling

Structure

Since we now have temporally dense time-series information we are going to make use of 1D convolutional layers. With temporally-dense information, an RNN has to study very lengthy dependencies with the intention to decide up on patterns, CNNs can merely stack a couple of convolutional layers to construct sample representations of considerable size. Since we’re additionally merely on the lookout for a single classification of exercise for every commentary, we will simply use pooling to ‘summarize’ the CNNs view of the information right into a dense layer.

Along with stacking two layer_conv_1d() layers, we are going to use batch norm and dropout (the spatial variant(Tompson et al. 2014) on the convolutional layers and commonplace on the dense) to regularize the community.

input_shape <- dim(trainObs)[-1]
num_classes <- dim(trainY)[2]

filters <- 24     # variety of convolutional filters to study
kernel_size <- 8  # what number of time-steps every conv layer sees.
dense_size <- 48  # measurement of our penultimate dense layer. 

# Initialize mannequin
mannequin <- keras_model_sequential()
mannequin %>% 
  layer_conv_1d(
    filters = filters,
    kernel_size = kernel_size, 
    input_shape = input_shape,
    padding = "legitimate", 
    activation = "relu"
  ) %>%
  layer_batch_normalization() %>%
  layer_spatial_dropout_1d(0.15) %>% 
  layer_conv_1d(
    filters = filters/2,
    kernel_size = kernel_size,
    activation = "relu",
  ) %>%
  # Apply common pooling:
  layer_global_average_pooling_1d() %>% 
  layer_batch_normalization() %>%
  layer_dropout(0.2) %>% 
  layer_dense(
    dense_size,
    activation = "relu"
  ) %>% 
  layer_batch_normalization() %>%
  layer_dropout(0.25) %>% 
  layer_dense(
    num_classes, 
    activation = "softmax",
    identify = "dense_output"
  ) 

abstract(mannequin)

______________________________________________________________________
Layer (kind)                   Output Form                Param #    
======================================================================
conv1d_1 (Conv1D)              (None, 327, 24)             1176       
______________________________________________________________________
batch_normalization_1 (BatchNo (None, 327, 24)             96         
______________________________________________________________________
spatial_dropout1d_1 (SpatialDr (None, 327, 24)             0          
______________________________________________________________________
conv1d_2 (Conv1D)              (None, 320, 12)             2316       
______________________________________________________________________
global_average_pooling1d_1 (Gl (None, 12)                  0          
______________________________________________________________________
batch_normalization_2 (BatchNo (None, 12)                  48         
______________________________________________________________________
dropout_1 (Dropout)            (None, 12)                  0          
______________________________________________________________________
dense_1 (Dense)                (None, 48)                  624        
______________________________________________________________________
batch_normalization_3 (BatchNo (None, 48)                  192        
______________________________________________________________________
dropout_2 (Dropout)            (None, 48)                  0          
______________________________________________________________________
dense_output (Dense)           (None, 6)                   294        
======================================================================
Complete params: 4,746
Trainable params: 4,578
Non-trainable params: 168
______________________________________________________________________

Coaching

Now we will practice the mannequin utilizing our take a look at and coaching information. Observe that we use callback_model_checkpoint() to make sure that we save solely the most effective variation of the mannequin (fascinating since in some unspecified time in the future in coaching the mannequin could start to overfit or in any other case cease enhancing).

# Compile mannequin
mannequin %>% compile(
  loss = "categorical_crossentropy",
  optimizer = "rmsprop",
  metrics = "accuracy"
)

trainHistory <- mannequin %>%
  match(
    x = trainObs, y = trainY,
    epochs = 350,
    validation_data = record(testObs, testY),
    callbacks = record(
      callback_model_checkpoint("best_model.h5", 
                                save_best_only = TRUE)
    )
  )

The mannequin is studying one thing! We get a good 94.4% accuracy on the validation information, not unhealthy with six attainable lessons to select from. Let’s look into the validation efficiency somewhat deeper to see the place the mannequin is messing up.

Analysis

Now that we now have a skilled mannequin let’s examine the errors that it made on our testing information. We will load the most effective mannequin from coaching based mostly upon validation accuracy after which take a look at every commentary, what the mannequin predicted, how excessive a likelihood it assigned, and the true exercise label.

# dataframe to get labels onto one-hot encoded prediction columns
oneHotToLabel <- activityLabels %>% 
  mutate(quantity = quantity - 7) %>% 
  filter(quantity >= 0) %>% 
  mutate(class = paste0("V",quantity + 1)) %>% 
  choose(-number)

# Load our greatest mannequin checkpoint
bestModel <- load_model_hdf5("best_model.h5")

tidyPredictionProbs <- bestModel %>% 
  predict(testObs) %>% 
  as_data_frame() %>% 
  mutate(obs = 1:n()) %>% 
  collect(class, prob, -obs) %>% 
  right_join(oneHotToLabel, by = "class")

predictionPerformance <- tidyPredictionProbs %>% 
  group_by(obs) %>% 
  summarise(
    highestProb = max(prob),
    predicted = label[prob == highestProb]
  ) %>% 
  mutate(
    reality = testData$activityName,
    appropriate = reality == predicted
  ) 

predictionPerformance %>% paged_table()

First, let’s take a look at how ‘assured’ the mannequin was by if the prediction was appropriate or not.

predictionPerformance %>% 
  mutate(end result = ifelse(appropriate, 'Appropriate', 'Incorrect')) %>% 
  ggplot(aes(highestProb)) +
  geom_histogram(binwidth = 0.01) +
  geom_rug(alpha = 0.5) +
  facet_grid(end result~.) +
  ggtitle("Chances related to prediction by correctness")

Reassuringly it appears the mannequin was, on common, much less assured about its classifications for the inaccurate outcomes than the right ones. (Though, the pattern measurement is just too small to say something definitively.)

Let’s see what actions the mannequin had the toughest time with utilizing a confusion matrix.

predictionPerformance %>% 
  group_by(reality, predicted) %>% 
  summarise(rely = n()) %>% 
  mutate(good = reality == predicted) %>% 
  ggplot(aes(x = reality,  y = predicted)) +
  geom_point(aes(measurement = rely, shade = good)) +
  geom_text(aes(label = rely), 
            hjust = 0, vjust = 0, 
            nudge_x = 0.1, nudge_y = 0.1) + 
  guides(shade = FALSE, measurement = FALSE) +
  theme_minimal()

We see that, because the preliminary visualization prompt, the mannequin had a little bit of bother with distinguishing between LIE_TO_SIT and LIE_TO_STAND lessons, together with the SIT_TO_LIE and STAND_TO_LIE, which even have comparable visible profiles.

Future instructions

The obvious future course to take this evaluation can be to aim to make the mannequin extra basic by working with extra of the equipped exercise varieties. One other fascinating course can be to not separate the recordings into distinct ‘observations’ however as a substitute hold them as one streaming set of information, very like an actual world deployment of a mannequin would work, and see how nicely a mannequin might classify streaming information and detect adjustments in exercise.