Classifying bodily exercise from smartphone knowledge

Introduction

On this publish we’ll describe easy methods to use smartphone accelerometer and gyroscope knowledge to foretell the bodily actions of the people carrying the telephones. The information used on this publish comes from the Smartphone-Based mostly Recognition of Human Actions and Postural Transitions Knowledge Set distributed by the College of California, Irvine. Thirty people had been tasked with performing varied primary actions with an connected smartphone recording motion utilizing an accelerometer and gyroscope.

Earlier than we start, let’s load the assorted 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 publish come from the Smartphone-Based mostly 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 info incorporates two totally different ‘elements.’ One which has been pre-processed utilizing varied characteristic extraction strategies corresponding to fast-fourier rework, 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 knowledge has been utilized. That is the info set we’ll use.

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

Exercise labels

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

activityLabels <- learn.desk("knowledge/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 knowledge set. The important thing for the columns is taken from the info 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(
  "knowledge/RawData/labels.txt",
  col.names = c("experiment", "userId", "exercise", "startPos", "endPos")
)

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

File names

Subsequent, let’s have a look at the precise recordsdata of the consumer knowledge supplied to us in RawData/

dataFiles <- listing.recordsdata("knowledge/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 knowledge the file incorporates: 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 knowledge

Earlier than we are able to do something with the info supplied we have to get it right into a model-friendly format. This implies we need to have a listing of observations, their class (or exercise label), and the info equivalent to the recording.

To acquire this we’ll scan by means of every of the recording recordsdata current in dataFiles, search for what observations are contained within the recording, extract these recordings and return the whole lot to a simple to mannequin with dataframe.

# Learn contents of single file to a dataframe with accelerometer and gyro knowledge.
readInData <- perform(experiment, userId){
  genFilePath = perform(kind) {
    paste0("knowledge/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 <- perform(curExperiment, curUserId) {
  
  # load sensor knowledge from file into dataframe
  allData <- readInData(curExperiment, curUserId)

  extractObservation <- perform(startPos, endPos){
    allData[startPos:endPos,]
  }
  
  # get remark places 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(
      knowledge = map2(startPos, endPos, extractObservation)
    ) %>% 
    choose(-startPos, -endPos)
}

# scan by means of all experiment and userId combos and collect knowledge 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 info

Now that now we have all the info loaded together with the experiment, userId, and exercise labels, we are able to discover the info set.

Size of recordings

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

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

The actual fact there’s such a distinction in size of recording between the totally different exercise sorts requires us to be a bit cautious with how we proceed. If we practice the mannequin on each class directly we’re going to need to 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 knowledge being simply padding-zeros. Due to this, we’ll match our mannequin to only 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 route could be trying to make use of one other structure corresponding to an RNN that may deal with variable size inputs and coaching it on all the info. Nevertheless, you’ll run the danger of the mannequin studying merely that if the remark is lengthy it’s more than likely one of many 4 longest lessons which might not generalize to a state of affairs the place you had been working this mannequin on a real-time-stream of knowledge.

Filtering actions

Based mostly on our work from above, let’s subset the info to only 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 info we may have a decent quantity of knowledge left upon which our mannequin can study.

Coaching/testing break up

Earlier than we go any additional into exploring the info for our mannequin, in an try to be as truthful as potential with our efficiency measures, we have to break up the info right into a practice and check set. Since every consumer carried out all actions simply as soon as (aside from one who solely did 10 of the 12 actions) by splitting on userId we’ll be certain that our mannequin sees new individuals completely once we check 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, dimension = 24)

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

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

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

Visualizing actions

Now that now we have trimmed our knowledge by eradicating actions and splitting off a check set, we are able to really visualize the info for every class to see if there’s any instantly discernible form that our mannequin might be able to decide up on.

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

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

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

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

So a minimum of within the accelerometer knowledge patterns positively emerge. One would think about that the mannequin could have hassle with the variations between LIE_TO_SIT and LIE_TO_STAND as they’ve an identical profile on common. The identical goes for SIT_TO_STAND and STAND_TO_SIT.

Preprocessing

Earlier than we are able to practice the neural community, we have to take a few steps to preprocess the info.

Padding observations

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

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

98% 
334 

Now we merely have to convert our listing of observations to matrices, then use the tremendous useful pad_sequences() perform 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$knowledge %>% convertToTensor()
testObs <- testData$knowledge %>% convertToTensor()
  
dim(trainObs)

[1] 286 334   6

Fantastic, we now have our knowledge in a pleasant neural-network-friendly format of a 3D tensor with dimensions (<num obs>, <sequence size>, <channels>).

One-hot encoding

There’s one very last thing we have to do earlier than we are able to practice our mannequin, and that’s flip our remark lessons from integers into one-hot, or dummy encoded, vectors. Fortunately, once more Keras has provided us with a really useful perform to just do this.

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

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

Modeling

Structure

Since now we have temporally dense time-series knowledge we’ll make use of 1D convolutional layers. With temporally-dense knowledge, an RNN has to study very lengthy dependencies so as 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 in search of a single classification of exercise for every remark, we are able to simply use pooling to ‘summarize’ the CNNs view of the info right into a dense layer.

Along with stacking two layer_conv_1d() layers, we’ll 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  # dimension 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",
    title = "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 are able to practice the mannequin utilizing our check and coaching knowledge. Be aware that we use callback_model_checkpoint() to make sure that we save solely one of the best 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 bettering).

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

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

The mannequin is studying one thing! We get a decent 94.4% accuracy on the validation knowledge, not dangerous with six potential lessons to select from. Let’s look into the validation efficiency a bit deeper to see the place the mannequin is messing up.

Analysis

Now that now we have a skilled mannequin let’s examine the errors that it made on our testing knowledge. We are able to load one of the best mannequin from coaching primarily based upon validation accuracy after which have a look at every remark, what the mannequin predicted, how excessive a chance 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,
    right = reality == predicted
  ) 

predictionPerformance %>% paged_table()

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

predictionPerformance %>% 
  mutate(consequence = ifelse(right, 'Appropriate', 'Incorrect')) %>% 
  ggplot(aes(highestProb)) +
  geom_histogram(binwidth = 0.01) +
  geom_rug(alpha = 0.5) +
  facet_grid(consequence~.) +
  ggtitle("Possibilities 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 dimension is simply 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(dimension = rely, coloration = good)) +
  geom_text(aes(label = rely), 
            hjust = 0, vjust = 0, 
            nudge_x = 0.1, nudge_y = 0.1) + 
  guides(coloration = FALSE, dimension = FALSE) +
  theme_minimal()

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

Future instructions

The obvious future route to take this evaluation could be to try to make the mannequin extra normal by working with extra of the provided exercise sorts. One other fascinating route could be to not separate the recordings into distinct ‘observations’ however as a substitute maintain them as one streaming set of knowledge, very similar to an actual world deployment of a mannequin would work, and see how properly a mannequin might classify streaming knowledge and detect modifications in exercise.