Classifying bodily exercise from smartphone knowledge

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Classifying bodily exercise from smartphone knowledge


Introduction

On this put up we’ll describe tips on how to use smartphone accelerometer and gyroscope knowledge to foretell the bodily actions of the people carrying the telephones. The info used on this put up 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 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 info used on this put up 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 accommodates two completely different ‘elements.’ One which has been pre-processed utilizing varied characteristic extraction methods resembling 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 knowledge has been utilized. That is the info set we are going to use.

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

Exercise labels

The info 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("knowledge/activity_labels.txt", 
                             col.names = c("quantity", "label")) 

activityLabels %>% kable(align = c("c", "l"))
1 WALKING
2 WALKING_UPSTAIRS
3 WALKING_DOWNSTAIRS
4 SITTING
5 STANDING
6 LAYING
7 STAND_TO_SIT
8 SIT_TO_STAND
9 SIT_TO_LIE
10 LIE_TO_SIT
11 STAND_TO_LIE
12 LIE_TO_STAND

Subsequent, we load within the labels key for the RawData. This file is an inventory 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 take a look at the precise recordsdata of the consumer knowledge supplied to us in RawData/


dataFiles <- record.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 accommodates: 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("sort", "experiment", "userId"), 
           take away = FALSE) %>% 
  mutate(
    experiment = str_remove(experiment, "exp"),
    userId = str_remove_all(userId, "consumer|.txt")
  ) %>% 
  unfold(sort, filePath)

fileInfo %>% head() %>% kable()
01 01 acc_exp01_user01.txt gyro_exp01_user01.txt
02 01 acc_exp02_user01.txt gyro_exp02_user01.txt
03 02 acc_exp03_user02.txt gyro_exp03_user02.txt
04 02 acc_exp04_user02.txt gyro_exp04_user02.txt
05 03 acc_exp05_user03.txt gyro_exp05_user03.txt
06 03 acc_exp06_user03.txt gyro_exp06_user03.txt

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 wish to have an inventory of observations, their class (or exercise label), and the info similar to the recording.

To acquire this we are going to scan via every of the recording recordsdata current in dataFiles, search for what observations are contained within the recording, extract these recordings and return all the things to a straightforward to mannequin with dataframe.


# Learn contents of single file to a dataframe with accelerometer and gyro knowledge.
readInData <- operate(experiment, userId){
  genFilePath = operate(sort) {
    paste0("knowledge/RawData/", sort, "_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 courses.

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

  extractObservation <- operate(startPos, endPos){
    allData[startPos:endPos,]
  }
  
  # get commentary 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 via 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 we now 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 take 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 may be such a distinction in size of recording between the completely different exercise varieties requires us to be a bit cautious with how we proceed. If we practice the mannequin on each class without delay 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 are going to match our mannequin to only the biggest ‘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 attention-grabbing future path can be making an attempt to make use of one other structure resembling an RNN that may deal with variable size inputs and coaching it on all the info. Nevertheless, you’d run the danger of the mannequin studying merely that if the commentary is lengthy it’s more than likely one of many 4 longest courses which might not generalize to a situation 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 can have a good 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 honest as attainable with our efficiency measures, we have to break up the info right into a practice and take a look at set. Since every consumer carried out all actions simply as soon as (except one who solely did 10 of the 12 actions) by splitting on userId we are going to be sure that our mannequin sees new individuals solely 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 knowledge. 
trainData <- filteredObservations %>% 
  filter(userId %in% trainIds)

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

Visualizing actions

Now that we now have trimmed our knowledge by eradicating actions and splitting off a take a look at 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(operate(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("sort", "path"), sep = "_") %>% 
  mutate(sort = ifelse(sort == "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 = path)) +
  geom_line(alpha = 0.2) +
  geom_smooth(se = FALSE, alpha = 0.7, measurement = 0.5) +
  facet_grid(sort ~ activityName, scales = "free_y") +
  theme_minimal() +
  theme( axis.textual content.x = element_blank() )

So not less than within the accelerometer knowledge patterns undoubtedly emerge. One would think about that the mannequin might 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 are going to resolve 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 it will 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 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$knowledge %>% convertToTensor()
testObs <- testData$knowledge %>% convertToTensor()
  
dim(trainObs)

[1] 286 334   6

Great, we now have our knowledge 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 are able to practice our mannequin, and that’s flip our commentary courses from integers into one-hot, or dummy encoded, vectors. Fortunately, once more Keras has provided 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 knowledge we are going to 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 commentary, 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 are going to use batch norm and dropout (the spatial variant(Tompson et al. 2014) on the convolutional layers and customary 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",
    title = "dense_output"
  ) 

abstract(mannequin)

______________________________________________________________________
Layer (sort)                   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 take a look at and coaching knowledge. Word that we use callback_model_checkpoint() to make sure that we save solely the perfect variation of the mannequin (fascinating since sooner or later in coaching the mannequin might 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 knowledge, not dangerous with six attainable courses to select from. Let’s look into the validation efficiency slightly 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 knowledge. We are able to load the perfect 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(
    fact = testData$activityName,
    appropriate = fact == 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(outcome = ifelse(appropriate, 'Right', 'Incorrect')) %>% 
  ggplot(aes(highestProb)) +
  geom_histogram(binwidth = 0.01) +
  geom_rug(alpha = 0.5) +
  facet_grid(outcome~.) +
  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 proper 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(fact, predicted) %>% 
  summarise(rely = n()) %>% 
  mutate(good = fact == predicted) %>% 
  ggplot(aes(x = fact,  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 advised, the mannequin had a little bit of hassle with distinguishing between LIE_TO_SIT and LIE_TO_STAND courses, together with the SIT_TO_LIE and STAND_TO_LIE, which even have related visible profiles.

Future instructions

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

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Graves, Alex. 2012. “Supervised Sequence Labelling.” In Supervised Sequence Labelling with Recurrent Neural Networks, 5–13. Springer.

Kononenko, Igor. 1989. “Bayesian Neural Networks.” Organic Cybernetics 61 (5). Springer: 361–70.

LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. 2015. “Deep Studying.” Nature 521 (7553). Nature Publishing Group: 436.

Reyes-Ortiz, Jorge-L, Luca Oneto, Albert Samà, Xavier Parra, and Davide Anguita. 2016. “Transition-Conscious Human Exercise Recognition Utilizing Smartphones.” Neurocomputing 171. Elsevier: 754–67.

Tompson, Jonathan, Ross Goroshin, Arjun Jain, Yann LeCun, and Christoph Bregler. 2014. “Environment friendly Object Localization Utilizing Convolutional Networks.” CoRR abs/1411.4280. http://arxiv.org/abs/1411.4280.

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