Human-Activity-Recogntion

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Human-Activity-Recogntion

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Human activity recognition, or HAR, is a challenging time series classification task. It involves predicting the movement of a person based on sensor data and traditionally involves deep domain expertise and methods from signal processing to correctly engineer features from the raw data in order to fit a machine learning model.

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# Human-Activity-Recogntion
Human activity recognition, or HAR, is a challenging time series classification task.  It involves predicting the movement of a person based on sensor data and traditionally involves deep domain expertise and methods from signal processing to correctly engineer features from the raw data in order to fit a machine learning model.
## Human Activity Recognition using CNN in Keras
This repository contains the code for a small project. The aim of this project is to create a simple Convolutional Neural Network (CNN) based Human Activity Recognition (HAR) system. This system uses the sensor data from a 3D accelerometer for `x`, `y` and `z` axis and recognize the activity of the user e.g. `Walking`, `Jogging`, going `Upstairs` or `Downstairs`, etc.
### Files
The repository contains following files.
* `HAR.py`, Python script file, containing the Keras implementation of the CNN based Human Activity Recognition (HAR) model,
* `actitracker_raw.txt`, Text file containing the dataset used in this experiment,
* `model.h5`, A pretrained model, trained on the training data,
* `evaluate_model.py`, Python script file, containing the evaluation script. This script evaluates the performance of the pretrained netowrk on the provided testData, 
* `testData.npy`, Python data file, containing the testing data used for the evaluation of the available pretrained model,
* `groundTruth.npy`, Python data file, containing the ground truth values for the corresponding outputs for for the test data and
* `README.md`.

### Tools Required

The code in this repository is created using Python 3.6. Furthermore, following libraries are required to run the code provided in this repository:
* `Keras 2.*`
* `Numpy`
* `Matplotlib`
* `Pandas`
* `sklearn`

### Dataset
In these experiments we used the `Actitracker` dataset, released by Wireless Sensor Data Mining (WISDM) lab and can be found at this [[link]](http://www.cis.fordham.edu/wisdm/dataset.php). The data provide in this database is collected from `36` users using a smartphone in there pocket at a sample rate of `20 Samples per second`. The data contains values for acceleration for `x`,`y` and `z` axes, while user performs six different activities in a controlled envoirnment. These activities include 
* `Downstairs`,
* `Jogging`, 
* `Sitting`,
* `Standing`,
* `Upstairs`, and
* `Walking`.

The dataset is not balanced and the distribution of the dataset with respect to the performed activities (class labels) is shown in the figure below.
<p align="center">
<img width="460" height="300" src="https://raw.githubusercontent.com/Shahnawax/HAR-CNN-Keras/master/dataset-distribution.png">
</p>

### Evaluation
A simple CNN based neural network is created using the topology in `HAR.py`. The dataset is splitted into two subgroups, `trainData` and `testData` with the ratio of `80` and `20`% respectively. The training data is further split into training and validation data with the same distribution. The HAR model created in `HAR.py` is then trained on the training data and validated on the validataion data. To evaluate the performance of this network, we write a script "evaluate_model.py". This script uses the `20%` of random samples in the dataset and tests the pretrained CNN model `model.h5`. Furhtermore, this script reports the percentage of the wrong predictions as error and creates a confusion matrix. The results show that the network has an average accuracy of 92.1 %. For further details, the confusion matrix for the HAR on the testData is shown in the figure below:
<p align="center">
<img width="460" height="300" src="https://raw.githubusercontent.com/Shahnawax/HAR-CNN-Keras/master/confusion_matrix.png">
</p>

### Related Problems

The HAR model provided in this work can be further extended to perform the user identification from walking activity. Accelerometer dataset from 22 indivduals can be downloaded from the following [[link]](http://archive.ics.uci.edu/ml/datasets/User+Identification+From+Walking+Activity)

# <a title="Activity Recognition" href="https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition" > LSTMs for Human Activity Recognition</a>

Human Activity Recognition (HAR) using smartphones dataset and an LSTM RNN. Classifying the type of movement amongst six categories:
- WALKING,
- WALKING_UPSTAIRS,
- WALKING_DOWNSTAIRS,
- SITTING,
- STANDING,
- LAYING.

Compared to a classical approach, using a Recurrent Neural Networks (RNN) with Long Short-Term Memory cells (LSTMs) require no or almost no feature engineering. Data can be fed directly into the neural network who acts like a black box, modeling the problem correctly. [Other research](https://archive.ics.uci.edu/ml/machine-learning-databases/00240/UCI%20HAR%20Dataset.names) on the activity recognition dataset can use a big amount of feature engineering, which is rather a signal processing approach combined with classical data science techniques. The approach here is rather very simple in terms of how much was the data preprocessed.

Let's use Google's neat Deep Learning library, TensorFlow, demonstrating the usage of an LSTM, a type of Artificial Neural Network that can process sequential data / time series.

## Dataset overview

6 activities recorded in the experiment with one of the participants:

## Details about the input data

I will be using an LSTM on the data to learn (as a cellphone attached on the waist) to recognise the type of activity that the user is doing. The dataset's description goes like this:

> The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used.

That said, I will use the almost raw data: only the gravity effect has been filtered out of the accelerometer  as a preprocessing step for another 3D feature as an input to help learning. If you'd ever want to extract the gravity by yourself, you could fork my code on using a [Butterworth Low-Pass Filter (LPF) in Python](https://github.com/guillaume-chevalier/filtering-stft-and-laplace-transform) and edit it to have the right cutoff frequency of 0.3 Hz which is a good frequency for activity recognition from body sensors.

## What is an RNN?

As explained in [this article](http://karpathy.github.io/2015/05/21/rnn-effectiveness/), an RNN takes many input vectors to process them and output other vectors. It can be roughly pictured like in the image below, imagining each rectangle has a vectorial depth and other special hidden quirks in the image below. **In our case, the "many to one" architecture is used**: we accept time series of [feature vectors](https://www.quora.com/What-do-samples-features-time-steps-mean-in-LSTM/answer/Guillaume-Chevalier-2) (one vector per [time step](https://www.quora.com/What-do-samples-features-time-steps-mean-in-LSTM/answer/Guillaume-Chevalier-2)) to convert them to a probability vector at the output for classification. Note that a "one to one" architecture would be a standard feedforward neural network.

> ![RNN Architectures](https://raw.githubusercontent.com/Neuraxio/Machine-Learning-Figures/master/rnn-architectures.png)

## What is an LSTM?

An LSTM is an improved RNN. It is more complex, but easier to train, avoiding what is called the vanishing gradient problem. I recommend [this article](http://colah.github.io/posts/2015-08-Understanding-LSTMs/) for you to learn more on LSTMs.

## Results

Scroll on! Nice visuals awaits.

```python
# All Includes

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import tensorflow as tf  # Version 1.0.0 (some previous versions are used in past commits)
from sklearn import metrics

import os
```

```python
# Useful Constants

# Those are separate normalised input features for the neural network
INPUT_SIGNAL_TYPES = [
    "body_acc_x_",
    "body_acc_y_",
    "body_acc_z_",
    "body_gyro_x_",
    "body_gyro_y_",
    "body_gyro_z_",
    "total_acc_x_",
    "total_acc_y_",
    "total_acc_z_"
]

# Output classes to learn how to classify
LABELS = [
    "WALKING",
    "WALKING_UPSTAIRS",
    "WALKING_DOWNSTAIRS",
    "SITTING",
    "STANDING",
    "LAYING"
]

```

## Let's start by downloading the data:

```python
# Note: Linux bash commands start with a "!" inside those "ipython notebook" cells

DATA_PATH = "data/"

!pwd && ls
os.chdir(DATA_PATH)
!pwd && ls

!python download_dataset.py

!pwd && ls
os.chdir("..")
!pwd && ls

DATASET_PATH = DATA_PATH + "UCI HAR Dataset/"
print("\n" + "Dataset is now located at: " + DATASET_PATH)

```

/home/ubuntu/pynb/LSTM-Human-Activity-Recognition
    data	 LSTM_files  LSTM_OLD.ipynb  README.md
    LICENSE  LSTM.ipynb  lstm.py	     screenlog.0
    /home/ubuntu/pynb/LSTM-Human-Activity-Recognition/data
    download_dataset.py  source.txt

Downloading...
    --2017-05-24 01:49:53--  https://archive.ics.uci.edu/ml/machine-learning-databases/00240/UCI%20HAR%20Dataset.zip
    Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.249
    Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.249|:443... connected.
    HTTP request sent, awaiting response... 200 OK
    Length: 60999314 (58M) [application/zip]
    Saving to: ‘UCI HAR Dataset.zip’

100%[======================================>] 60,999,314  1.69MB/s   in 38s

2017-05-24 01:50:31 (1.55 MB/s) - ‘UCI HAR Dataset.zip’ saved [60999314/60999314]

Downloading done.

Extracting...
    Extracting successfully done to /home/ubuntu/pynb/LSTM-Human-Activity-Recognition/data/UCI HAR Dataset.
    /home/ubuntu/pynb/LSTM-Human-Activity-Recognition/data
    download_dataset.py  __MACOSX  source.txt  UCI HAR Dataset  UCI HAR Dataset.zip
    /home/ubuntu/pynb/LSTM-Human-Activity-Recognition
    data	 LSTM_files  LSTM_OLD.ipynb  README.md
    LICENSE  LSTM.ipynb  lstm.py	     screenlog.0

Dataset is now located at: data/UCI HAR Dataset/

## Preparing dataset:

```python
TRAIN = "train/"
TEST = "test/"

# Load "X" (the neural network's training and testing inputs)

def load_X(X_signals_paths):
    X_signals = []

for signal_type_path in X_signals_paths:
        file = open(signal_type_path, 'r')
        # Read dataset from disk, dealing with text files' syntax
        X_signals.append(
            [np.array(serie, dtype=np.float32) for serie in [
                row.replace('  ', ' ').strip().split(' ') for row in file
            ]]
        )
        file.close()

return np.transpose(np.array(X_signals), (1, 2, 0))

X_train_signals_paths = [
    DATASET_PATH + TRAIN + "Inertial Signals/" + signal + "train.txt" for signal in INPUT_SIGNAL_TYPES
]
X_test_signals_paths = [
    DATASET_PATH + TEST + "Inertial Signals/" + signal + "test.txt" for signal in INPUT_SIGNAL_TYPES
]

X_train = load_X(X_train_signals_paths)
X_test = load_X(X_test_signals_paths)

# Load "y" (the neural network's training and testing outputs)

def load_y(y_path):
    file = open(y_path, 'r')
    # Read dataset from disk, dealing with text file's syntax
    y_ = np.array(
        [elem for elem in [
            row.replace('  ', ' ').strip().split(' ') for row in file
        ]],
        dtype=np.int32
    )
    file.close()

# Substract 1 to each output class for friendly 0-based indexing
    return y_ - 1

y_train_path = DATASET_PATH + TRAIN + "y_train.txt"
y_test_path = DATASET_PATH + TEST + "y_test.txt"

y_train = load_y(y_train_path)
y_test = load_y(y_test_path)

```

## Additionnal Parameters:

Here are some core parameter definitions for the training.

For example, the whole neural network's structure could be summarised by enumerating those parameters and the fact that two LSTM are used one on top of another (stacked) output-to-input as hidden layers through time steps.

```python
# Input Data

training_data_count = len(X_train)  # 7352 training series (with 50% overlap between each serie)
test_data_count = len(X_test)  # 2947 testing series
n_steps = len(X_train[0])  # 128 timesteps per series
n_input = len(X_train[0][0])  # 9 input parameters per timestep

# LSTM Neural Network's internal structure

n_hidden = 32 # Hidden layer num of features
n_classes = 6 # Total classes (should go up, or should go down)

# Training

learning_rate = 0.0025
lambda_loss_amount = 0.0015
training_iters = training_data_count * 300  # Loop 300 times on the dataset
batch_size = 1500
display_iter = 30000  # To show test set accuracy during training

# Some debugging info

print("Some useful info to get an insight on dataset's shape and normalisation:")
print("(X shape, y shape, every X's mean, every X's standard deviation)")
print(X_test.shape, y_test.shape, np.mean(X_test), np.std(X_test))
print("The dataset is therefore properly normalised, as expected, but not yet one-hot encoded.")

```

Some useful info to get an insight on dataset's shape and normalisation:
    (X shape, y shape, every X's mean, every X's standard deviation)
    (2947, 128, 9) (2947, 1) 0.0991399 0.395671
    The dataset is therefore properly normalised, as expected, but not yet one-hot encoded.

## Utility functions for training:

```python
def LSTM_RNN(_X, _weights, _biases):
    # Function returns a tensorflow LSTM (RNN) artificial neural network from given parameters.
    # Moreover, two LSTM cells are stacked which adds deepness to the neural network.
    # Note, some code of this notebook is inspired from an slightly different
    # RNN architecture used on another dataset, some of the credits goes to
    # "aymericdamien" under the MIT license.

# (NOTE: This step could be greatly optimised by shaping the dataset once
    # input shape: (batch_size, n_steps, n_input)
    _X = tf.transpose(_X, [1, 0, 2])  # permute n_steps and batch_size
    # Reshape to prepare input to hidden activation
    _X = tf.reshape(_X, [-1, n_input])
    # new shape: (n_steps*batch_size, n_input)

# ReLU activation, thanks to Yu Zhao for adding this improvement here:
    _X = tf.nn.relu(tf.matmul(_X, _weights['hidden']) + _biases['hidden'])
    # Split data because rnn cell needs a list of inputs for the RNN inner loop
    _X = tf.split(_X, n_steps, 0)
    # new shape: n_steps * (batch_size, n_hidden)

# Define two stacked LSTM cells (two recurrent layers deep) with tensorflow
    lstm_cell_1 = tf.contrib.rnn.BasicLSTMCell(n_hidden, forget_bias=1.0, state_is_tuple=True)
    lstm_cell_2 = tf.contrib.rnn.BasicLSTMCell(n_hidden, forget_bias=1.0, state_is_tuple=True)
    lstm_cells = tf.contrib.rnn.MultiRNNCell([lstm_cell_1, lstm_cell_2], state_is_tuple=True)
    # Get LSTM cell output
    outputs, states = tf.contrib.rnn.static_rnn(lstm_cells, _X, dtype=tf.float32)

# Get last time step's output feature for a "many-to-one" style classifier,
    # as in the image describing RNNs at the top of this page
    lstm_last_output = outputs[-1]

# Linear activation
    return tf.matmul(lstm_last_output, _weights['out']) + _biases['out']

def extract_batch_size(_train, step, batch_size):
    # Function to fetch a "batch_size" amount of data from "(X|y)_train" data.

shape = list(_train.shape)
    shape[0] = batch_size
    batch_s = np.empty(shape)

for i in range(batch_size):
        # Loop index
        index = ((step-1)*batch_size + i) % len(_train)
        batch_s[i] = _train[index]

return batch_s

def one_hot(y_, n_classes=n_classes):
    # Function to encode neural one-hot output labels from number indexes
    # e.g.:
    # one_hot(y_=[[5], [0], [3]], n_classes=6):
    #     return [[0, 0, 0, 0, 0, 1], [1, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0]]

y_ = y_.reshape(len(y_))
    return np.eye(n_classes)[np.array(y_, dtype=np.int32)]  # Returns FLOATS

```

## Let's get serious and build the neural network:

```python

# Graph input/output
x = tf.placeholder(tf.float32, [None, n_steps, n_input])
y = tf.placeholder(tf.float32, [None, n_classes])

# Graph weights
weights = {
    'hidden': tf.Variable(tf.random_normal([n_input, n_hidden])), # Hidden layer weights
    'out': tf.Variable(tf.random_normal([n_hidden, n_classes], mean=1.0))
}
biases = {
    'hidden': tf.Variable(tf.random_normal([n_hidden])),
    'out': tf.Variable(tf.random_normal([n_classes]))
}

pred = LSTM_RNN(x, weights, biases)

# Loss, optimizer and evaluation
l2 = lambda_loss_amount * sum(
    tf.nn.l2_loss(tf_var) for tf_var in tf.trainable_variables()
) # L2 loss prevents this overkill neural network to overfit the data
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=pred)) + l2 # Softmax loss
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost) # Adam Optimizer

correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

```

## Hooray, now train the neural network:

```python
# To keep track of training's performance
test_losses = []
test_accuracies = []
train_losses = []
train_accuracies = []

# Launch the graph
sess = tf.InteractiveSession(config=tf.ConfigProto(log_device_placement=True))
init = tf.global_variables_initializer()
sess.run(init)

# Perform Training steps with "batch_size" amount of example data at each loop
step = 1
while step * batch_size <= training_iters:
    batch_xs =         extract_batch_size(X_train, step, batch_size)
    batch_ys = one_hot(extract_batch_size(y_train, step, batch_size))

# Fit training using batch data
    _, loss, acc = sess.run(
        [optimizer, cost, accuracy],
        feed_dict={
            x: batch_xs,
            y: batch_ys
        }
    )
    train_losses.append(loss)
    train_accuracies.append(acc)

# Evaluate network only at some steps for faster training:
    if (step*batch_size % display_iter == 0) or (step == 1) or (step * batch_size > training_iters):

# To not spam console, show training accuracy/loss in this "if"
        print("Training iter #" + str(step*batch_size) + \
              ":   Batch Loss = " + "{:.6f}".format(loss) + \
              ", Accuracy = {}".format(acc))

# Evaluation on the test set (no learning made here - just evaluation for diagnosis)
        loss, acc = sess.run(
            [cost, accuracy],
            feed_dict={
                x: X_test,
                y: one_hot(y_test)
            }
        )
        test_losses.append(loss)
        test_accuracies.append(acc)
        print("PERFORMANCE ON TEST SET: " + \
              "Batch Loss = {}".format(loss) + \
              ", Accuracy = {}".format(acc))

step += 1

print("Optimization Finished!")

# Accuracy for test data

one_hot_predictions, accuracy, final_loss = sess.run(
    [pred, accuracy, cost],
    feed_dict={
        x: X_test,
        y: one_hot(y_test)
    }
)

test_losses.append(final_loss)
test_accuracies.append(accuracy)

print("FINAL RESULT: " + \
      "Batch Loss = {}".format(final_loss) + \
      ", Accuracy = {}".format(accuracy))

```

WARNING:tensorflow:From <ipython-input-19-3339689e51f6>:9: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.
    Instructions for updating:
    Use `tf.global_variables_initializer` instead.
    Training iter #1500:   Batch Loss = 5.416760, Accuracy = 0.15266665816307068
    PERFORMANCE ON TEST SET: Batch Loss = 4.880829811096191, Accuracy = 0.05632847175002098
    Training iter #30000:   Batch Loss = 3.031930, Accuracy = 0.607333242893219
    PERFORMANCE ON TEST SET: Batch Loss = 3.0515167713165283, Accuracy = 0.6067186594009399
    Training iter #60000:   Batch Loss = 2.672764, Accuracy = 0.7386666536331177
    PERFORMANCE ON TEST SET: Batch Loss = 2.780435085296631, Accuracy = 0.7027485370635986
    Training iter #90000:   Batch Loss = 2.378301, Accuracy = 0.8366667032241821
    PERFORMANCE ON TEST SET: Batch Loss = 2.6019773483276367, Accuracy = 0.7617915868759155
    Training iter #120000:   Batch Loss = 2.127290, Accuracy = 0.9066667556762695
    PERFORMANCE ON TEST SET: Batch Loss = 2.3625404834747314, Accuracy = 0.8116728663444519
    Training iter #150000:   Batch Loss = 1.929805, Accuracy = 0.9380000233650208
    PERFORMANCE ON TEST SET: Batch Loss = 2.306251049041748, Accuracy = 0.8276212215423584
    Training iter #180000:   Batch Loss = 1.971904, Accuracy = 0.9153333902359009
    PERFORMANCE ON TEST SET: Batch Loss = 2.0835530757904053, Accuracy = 0.8771631121635437
    Training iter #210000:   Batch Loss = 1.860249, Accuracy = 0.8613333702087402
    PERFORMANCE ON TEST SET: Batch Loss = 1.9994492530822754, Accuracy = 0.8788597583770752
    Training iter #240000:   Batch Loss = 1.626292, Accuracy = 0.9380000233650208
    PERFORMANCE ON TEST SET: Batch Loss = 1.879166603088379, Accuracy = 0.8944689035415649
    Training iter #270000:   Batch Loss = 1.582758, Accuracy = 0.9386667013168335
    PERFORMANCE ON TEST SET: Batch Loss = 2.0341007709503174, Accuracy = 0.8361043930053711
    Training iter #300000:   Batch Loss = 1.620352, Accuracy = 0.9306666851043701
    PERFORMANCE ON TEST SET: Batch Loss = 1.8185184001922607, Accuracy = 0.8639293313026428
    Training iter #330000:   Batch Loss = 1.474394, Accuracy = 0.9693333506584167
    PERFORMANCE ON TEST SET: Batch Loss = 1.7638503313064575, Accuracy = 0.8747878670692444
    Training iter #360000:   Batch Loss = 1.406998, Accuracy = 0.9420000314712524
    PERFORMANCE ON TEST SET: Batch Loss = 1.5946787595748901, Accuracy = 0.902273416519165
    Training iter #390000:   Batch Loss = 1.362515, Accuracy = 0.940000057220459
    PERFORMANCE ON TEST SET: Batch Loss = 1.5285792350769043, Accuracy = 0.9046487212181091
    Training iter #420000:   Batch Loss = 1.252860, Accuracy = 0.9566667079925537
    PERFORMANCE ON TEST SET: Batch Loss = 1.4635565280914307, Accuracy = 0.9107565879821777
    Training iter #450000:   Batch Loss = 1.190078, Accuracy = 0.9553333520889282
    ...
    PERFORMANCE ON TEST SET: Batch Loss = 0.42567864060401917, Accuracy = 0.9324736595153809
    Training iter #2070000:   Batch Loss = 0.342763, Accuracy = 0.9326667189598083
    PERFORMANCE ON TEST SET: Batch Loss = 0.4292983412742615, Accuracy = 0.9273836612701416
    Training iter #2100000:   Batch Loss = 0.259442, Accuracy = 0.9873334169387817
    PERFORMANCE ON TEST SET: Batch Loss = 0.44131210446357727, Accuracy = 0.9273836612701416
    Training iter #2130000:   Batch Loss = 0.284630, Accuracy = 0.9593333601951599
    PERFORMANCE ON TEST SET: Batch Loss = 0.46982717514038086, Accuracy = 0.9093992710113525
    Training iter #2160000:   Batch Loss = 0.299012, Accuracy = 0.9686667323112488
    PERFORMANCE ON TEST SET: Batch Loss = 0.48389002680778503, Accuracy = 0.9138105511665344
    Training iter #2190000:   Batch Loss = 0.287106, Accuracy = 0.9700000286102295
    PERFORMANCE ON TEST SET: Batch Loss = 0.4670214056968689, Accuracy = 0.9216151237487793
    Optimization Finished!
    FINAL RESULT: Batch Loss = 0.45611169934272766, Accuracy = 0.9165252447128296

## Training is good, but having visual insight is even better:

Okay, let's plot this simply in the notebook for now.

```python
# (Inline plots: )
%matplotlib inline

font = {
    'family' : 'Bitstream Vera Sans',
    'weight' : 'bold',
    'size'   : 18
}
matplotlib.rc('font', **font)

width = 12
height = 12
plt.figure(figsize=(width, height))

indep_train_axis = np.array(range(batch_size, (len(train_losses)+1)*batch_size, batch_size))
plt.plot(indep_train_axis, np.array(train_losses),     "b--", label="Train losses")
plt.plot(indep_train_axis, np.array(train_accuracies), "g--", label="Train accuracies")

indep_test_axis = np.append(
    np.array(range(batch_size, len(test_losses)*display_iter, display_iter)[:-1]),
    [training_iters]
)
plt.plot(indep_test_axis, np.array(test_losses),     "b-", label="Test losses")
plt.plot(indep_test_axis, np.array(test_accuracies), "g-", label="Test accuracies")

plt.title("Training session's progress over iterations")
plt.legend(loc='upper right', shadow=True)
plt.ylabel('Training Progress (Loss or Accuracy values)')
plt.xlabel('Training iteration')

plt.show()
```

![LSTM Training Testing Comparison Curve](https://raw.githubusercontent.com/kakshak07/Human-Activity-Recogntion/master/LSTM_files/LSTM_16_0.png)

## And finally, the multi-class confusion matrix and metrics!

```python
# Results

predictions = one_hot_predictions.argmax(1)

print("Testing Accuracy: {}%".format(100*accuracy))

print("")
print("Precision: {}%".format(100*metrics.precision_score(y_test, predictions, average="weighted")))
print("Recall: {}%".format(100*metrics.recall_score(y_test, predictions, average="weighted")))
print("f1_score: {}%".format(100*metrics.f1_score(y_test, predictions, average="weighted")))

print("")
print("Confusion Matrix:")
confusion_matrix = metrics.confusion_matrix(y_test, predictions)
print(confusion_matrix)
normalised_confusion_matrix = np.array(confusion_matrix, dtype=np.float32)/np.sum(confusion_matrix)*100

print("")
print("Confusion matrix (normalised to % of total test data):")
print(normalised_confusion_matrix)
print("Note: training and testing data is not equally distributed amongst classes, ")
print("so it is normal that more than a 6th of the data is correctly classifier in the last category.")

# Plot Results:
width = 12
height = 12
plt.figure(figsize=(width, height))
plt.imshow(
    normalised_confusion_matrix,
    interpolation='nearest',
    cmap=plt.cm.rainbow
)
plt.title("Confusion matrix \n(normalised to % of total test data)")
plt.colorbar()
tick_marks = np.arange(n_classes)
plt.xticks(tick_marks, LABELS, rotation=90)
plt.yticks(tick_marks, LABELS)
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.show()
```

Testing Accuracy: 91.65252447128296%

Precision: 91.76286479743305%
    Recall: 91.65252799457076%
    f1_score: 91.6437546304815%

Confusion Matrix:
    [[466   2  26   0   2   0]
     [  5 441  25   0   0   0]
     [  1   0 419   0   0   0]
     [  1   1   0 396  87   6]
     [  2   1   0  87 442   0]
     [  0   0   0   0   0 537]]

Confusion matrix (normalised to % of total test data):
    [[ 15.81269073   0.06786563   0.88225317   0.           0.06786563   0.        ]
     [  0.16966406  14.96437073   0.84832031   0.           0.           0.        ]
     [  0.03393281   0.          14.21784878   0.           0.           0.        ]
     [  0.03393281   0.03393281   0.          13.43739319   2.95215464
        0.20359688]
     [  0.06786563   0.03393281   0.           2.95215464  14.99830341   0.        ]
     [  0.           0.           0.           0.           0.          18.22192001]]
    Note: training and testing data is not equally distributed amongst classes,
    so it is normal that more than a 6th of the data is correctly classifier in the last category.

![Confusion Matrix](https://raw.githubusercontent.com/kakshak07/Human-Activity-Recogntion/master/LSTM_files/LSTM_18_1.png)

```python
sess.close()
```

## Conclusion

Outstandingly, **the final accuracy is of 91%**! And it can peak to values such as 93.25%, at some moments of luck during the training, depending on how the neural network's weights got initialized at the start of the training, randomly.

I specially did not expect such good results for guessing between the labels "SITTING" and "STANDING". Those are seemingly almost the same thing from the point of view of a device placed at waist level according to how the dataset was originally gathered. Thought, it is still possible to see a little cluster on the matrix between those classes, which drifts away just a bit from the identity. This is great.

It is also possible to see that there was a slight difficulty in doing the difference between "WALKING", "WALKING_UPSTAIRS" and "WALKING_DOWNSTAIRS". Obviously, those activities are quite similar in terms of movements.

## References

The [dataset](https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones) can be found on the UCI Machine Learning Repository:

> Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. A Public Domain Dataset for Human Activity Recognition Using Smartphones. 21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium 24-26 April 2013.

Please feel free to contact me: kakshak@gmail.com

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