Ever wondered how your smartphone, smartwatch or wristband knows when you’re walking, running or sitting? Well, your device probably has multiple sensors that give various information. GPS, audio (i.e. microphones), image (i.e. cameras), direction (i.e. compasses) and acceleration sensors are very common nowadays.

source: Mathworks

We will use data collected from accelerometer sensors. Virtually every modern smartphone has a tri-axial accelerometer that measures acceleration in all three spatial dimensions. Additionally, accelerometers can detect device orientation.

In this part of the series, we will train an LSTM Neural Network (implemented in TensorFlow) for Human Activity Recognition (HAR) from accelerometer data. The trained model will be exported/saved and added to an Android app. We will learn how to use it for inference from Java.

The source code for this part is available (including the Android app) on GitHub.

# The data

We will use data provided by the Wireless Sensor Data Mining (WISDM) Lab. It can be download from here. The dataset was collected in controlled, laboratory setting. The lab provides another dataset collected from real-world usage of a smartphone app. You’re free to use/explore it as well. Here’s a video that presents how a similar dataset was collected:

Our dataset contains 1,098,207 rows and 6 columns. There are no missing values. There are 6 activities that we’ll try to recognize: Walking, Jogging, Upstairs, Downstairs, Sitting, Standing. Let’s have a closer look at the data:

import pandas as pd
import numpy as np
import pickle
import matplotlib.pyplot as plt
from scipy import stats
import tensorflow as tf
import seaborn as sns
from pylab import rcParams
from sklearn import metrics
from sklearn.model_selection import train_test_split

%matplotlib inline

sns.set(style='whitegrid', palette='muted', font_scale=1.5)

rcParams['figure.figsize'] = 14, 8

RANDOM_SEED = 42

columns = ['user','activity','timestamp', 'x-axis', 'y-axis', 'z-axis']
df = pd.read_csv('data/WISDM_ar_v1.1_raw.txt', header = None, names = columns)
df = df.dropna()

df.head()

user activity timestamp x-axis y-axis z-axis
0 33 Jogging 49105962326000 -0.694638 12.680544 0.503953
1 33 Jogging 49106062271000 5.012288 11.264028 0.953424
2 33 Jogging 49106112167000 4.903325 10.882658 -0.081722
3 33 Jogging 49106222305000 -0.612916 18.496431 3.023717
4 33 Jogging 49106332290000 -1.184970 12.108489 7.205164

# Exploration

The columns we will be most interested in are activity, x-axis, y-axis and z-axis. Let’s dive into the data:

df['activity'].value_counts().plot(kind='bar', title='Training examples by activity type');


The columns we will be most interested in are activity, x-axis, y-axis and z-axis. Let’s dive into the data:

df['user'].value_counts().plot(kind='bar', title='Training examples by user');


I wonder whether or not number 4 received the same paycheck as number 20. Now, for some accelerometer data:

def plot_activity(activity, df):
data = df[df['activity'] == activity][['x-axis', 'y-axis', 'z-axis']][:200]
axis = data.plot(subplots=True, figsize=(16, 12),
title=activity)
for ax in axis:
ax.legend(loc='lower left', bbox_to_anchor=(1.0, 0.5))

plot_activity("Sitting", df)


plot_activity("Standing", df)


plot_activity("Walking", df)


plot_activity("Jogging", df)


It seems reasonable to assume that this data might be used to train a model that can distinguish between the different kinds of activities. Well, at least the first 200 entries of each activity look that way.

# Data preprocessing

Our LSTM (covered in the previous part of the series) model expects fixed-length sequences as training data. We’ll use a familiar method for generating these. Each generated sequence contains 200 training examples:

N_TIME_STEPS = 200
N_FEATURES = 3
step = 20
segments = []
labels = []
for i in range(0, len(df) - N_TIME_STEPS, step):
xs = df['x-axis'].values[i: i + N_TIME_STEPS]
ys = df['y-axis'].values[i: i + N_TIME_STEPS]
zs = df['z-axis'].values[i: i + N_TIME_STEPS]
label = stats.mode(df['activity'][i: i + N_TIME_STEPS])[0][0]
segments.append([xs, ys, zs])
labels.append(label)

np.array(segments).shape

(54901, 3, 200)


Our training dataset has drastically reduced size after the transformation. Note that we take the most common activity and assign it as a label for the sequence.

The shape of our tensor looks kinda strange. Let’s transform it into sequences of 200 rows, each containing x, y and z. Let’s apply a one-hot encoding to our labels, as well:

reshaped_segments = np.asarray(segments, dtype= np.float32).reshape(-1, N_TIME_STEPS, N_FEATURES)
labels = np.asarray(pd.get_dummies(labels), dtype = np.float32)

reshaped_segments.shape

(54901, 200, 3)

labels[0]

array([ 0.,  1.,  0.,  0.,  0.,  0.], dtype=float32)


Finally, let’s split the data into training and test (20%) set:

X_train, X_test, y_train, y_test = train_test_split(
reshaped_segments, labels, test_size=0.2, random_state=RANDOM_SEED)


# Building the model

Our model contains 2 fully-connected and 2 LSTM layers (stacked on each other) with 64 units each:

N_CLASSES = 6
N_HIDDEN_UNITS = 64

def create_LSTM_model(inputs):
W = {
'hidden': tf.Variable(tf.random_normal([N_FEATURES, N_HIDDEN_UNITS])),
'output': tf.Variable(tf.random_normal([N_HIDDEN_UNITS, N_CLASSES]))
}
biases = {
'hidden': tf.Variable(tf.random_normal([N_HIDDEN_UNITS], mean=1.0)),
'output': tf.Variable(tf.random_normal([N_CLASSES]))
}

X = tf.transpose(inputs, [1, 0, 2])
X = tf.reshape(X, [-1, N_FEATURES])
hidden = tf.nn.relu(tf.matmul(X, W['hidden']) + biases['hidden'])
hidden = tf.split(hidden, N_TIME_STEPS, 0)

# Stack 2 LSTM layers
lstm_layers = [tf.contrib.rnn.BasicLSTMCell(N_HIDDEN_UNITS, forget_bias=1.0) for _ in range(2)]
lstm_layers = tf.contrib.rnn.MultiRNNCell(lstm_layers)

outputs, _ = tf.contrib.rnn.static_rnn(lstm_layers, hidden, dtype=tf.float32)

# Get output for the last time step
lstm_last_output = outputs[-1]

return tf.matmul(lstm_last_output, W['output']) + biases['output']


Now, let create placeholders for our model:

tf.reset_default_graph()

X = tf.placeholder(tf.float32, [None, N_TIME_STEPS, N_FEATURES], name="input")
Y = tf.placeholder(tf.float32, [None, N_CLASSES])


Note that we named the input tensor, that will be useful when using the model from Android. Creating the model:

pred_Y = create_LSTM_model(X)

pred_softmax = tf.nn.softmax(pred_Y, name="y_")


Again, we must properly name the tensor from which we will obtain predictions. We will use L2 regularization and that must be noted in our loss op:

L2_LOSS = 0.0015

l2 = L2_LOSS * \
sum(tf.nn.l2_loss(tf_var) for tf_var in tf.trainable_variables())

loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = pred_Y, labels = Y)) + l2


Finally, let’s define optimizer and accuracy ops:

LEARNING_RATE = 0.0025

correct_pred = tf.equal(tf.argmax(pred_softmax, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, dtype=tf.float32))


# Training

The training part contains a lot of TensorFlow boilerplate. We will train our model for 50 epochs and keep track of accuracy and error:

N_EPOCHS = 50
BATCH_SIZE = 1024

saver = tf.train.Saver()

history = dict(train_loss=[],
train_acc=[],
test_loss=[],
test_acc=[])

sess=tf.InteractiveSession()
sess.run(tf.global_variables_initializer())

train_count = len(X_train)

for i in range(1, N_EPOCHS + 1):
for start, end in zip(range(0, train_count, BATCH_SIZE),
range(BATCH_SIZE, train_count + 1,BATCH_SIZE)):
sess.run(optimizer, feed_dict={X: X_train[start:end],
Y: y_train[start:end]})

_, acc_train, loss_train = sess.run([pred_softmax, accuracy, loss], feed_dict={
X: X_train, Y: y_train})

_, acc_test, loss_test = sess.run([pred_softmax, accuracy, loss], feed_dict={
X: X_test, Y: y_test})

history['train_loss'].append(loss_train)
history['train_acc'].append(acc_train)
history['test_loss'].append(loss_test)
history['test_acc'].append(acc_test)

if i != 1 and i % 10 != 0:
continue

print(f'epoch: {i} test accuracy: {acc_test} loss: {loss_test}')

predictions, acc_final, loss_final = sess.run([pred_softmax, accuracy, loss], feed_dict={X: X_test, Y: y_test})

print()
print(f'final results: accuracy: {acc_final} loss: {loss_final}')

epoch: 1 test accuracy: 0.7736998796463013 loss: 1.2773654460906982
epoch: 10 test accuracy: 0.9388942122459412 loss: 0.5612533092498779
epoch: 20 test accuracy: 0.9574717283248901 loss: 0.3916512429714203
epoch: 30 test accuracy: 0.9693103432655334 loss: 0.2935260236263275
epoch: 40 test accuracy: 0.9747744202613831 loss: 0.2502188980579376


Whew, that was a lot of training. Do you feel thirsty? Let’s store our precious model to disk:

pickle.dump(predictions, open("predictions.p", "wb"))
pickle.dump(history, open("history.p", "wb"))
tf.train.write_graph(sess.graph_def, '.', './checkpoint/har.pbtxt')
saver.save(sess, save_path = "./checkpoint/har.ckpt")
sess.close()


history = pickle.load(open("history.p", "rb"))
predictions = pickle.load(open("predictions.p", "rb"))


# Evaluation

plt.figure(figsize=(12, 8))

plt.plot(np.array(history['train_loss']), "r--", label="Train loss")
plt.plot(np.array(history['train_acc']), "g--", label="Train accuracy")

plt.plot(np.array(history['test_loss']), "r-", label="Test loss")
plt.plot(np.array(history['test_acc']), "g-", label="Test accuracy")

plt.title("Training session's progress over iterations")
plt.ylabel('Training Progress (Loss or Accuracy values)')
plt.xlabel('Training Epoch')
plt.ylim(0)

plt.show()


Our model seems to learn well with accuracy reaching above 97% and loss hovering at around 0.2. Let’s have a look at the confusion matrix for the model’s predictions:

LABELS = ['Downstairs', 'Jogging', 'Sitting', 'Standing', 'Upstairs', 'Walking']

max_test = np.argmax(y_test, axis=1)
max_predictions = np.argmax(predictions, axis=1)
confusion_matrix = metrics.confusion_matrix(max_test, max_predictions)

plt.figure(figsize=(16, 14))
sns.heatmap(confusion_matrix, xticklabels=LABELS, yticklabels=LABELS, annot=True, fmt="d");
plt.title("Confusion matrix")
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.show();


Again, it looks like our model performs real good. Some notable exceptions include the misclassification of Upstairs for Downstairs and vice versa. Jogging seems to fail us from time to time as well!

# Exporting the model

Now that most of the hard work is done we must export our model in a way that TensorFlow for Android will understand it:

from tensorflow.python.tools import freeze_graph

MODEL_NAME = 'har'

input_graph_path = 'checkpoint/' + MODEL_NAME+'.pbtxt'
checkpoint_path = './checkpoint/' +MODEL_NAME+'.ckpt'
restore_op_name = "save/restore_all"
filename_tensor_name = "save/Const:0"
output_frozen_graph_name = 'frozen_'+MODEL_NAME+'.pb'

freeze_graph.freeze_graph(input_graph_path, input_saver="",
input_binary=False, input_checkpoint=checkpoint_path,
output_node_names="y_", restore_op_name="save/restore_all",
filename_tensor_name="save/Const:0",
output_graph=output_frozen_graph_name, clear_devices=True, initializer_nodes="")

INFO:tensorflow:Restoring parameters from ./checkpoint/har.ckpt
INFO:tensorflow:Froze 8 variables.
Converted 8 variables to const ops.
6862 ops in the final graph.


A sample app that uses the exported model can be found on GitHub. It is based heavily based on the Activity Recognition app by Aaqib Saeed. Our app uses the text-to-speech Android API to tell you what the model predicts at some interval and includes our pre-trained model.

The most notable parts of the Java code include defining our input and output dimensions and names:

String INPUT_NODE = "inputs";
String[] OUTPUT_NODES = {"y_"};
String OUTPUT_NODE = "y_";
long[] INPUT_SIZE = {1, 200, 3};
int OUTPUT_SIZE = 6;


Creating the TensorFlowInferenceInterface:

inferenceInterface = new TensorFlowInferenceInterface(context.getAssets(), MODEL_FILE);


And making the predictions:

public float[] predictProbabilities(float[] data) {
float[] result = new float[OUTPUT_SIZE];
inferenceInterface.feed(INPUT_NODE, data, INPUT_SIZE);
inferenceInterface.run(OUTPUT_NODES);
inferenceInterface.fetch(OUTPUT_NODE, result);

//Downstairs	Jogging	  Sitting	Standing	Upstairs	Walking
return result;
}


The result is a float array that contains the probability for each possible activity, according to our model.

# Conclusion

We’ve built an LSTM model that can predict human activity from 200 time-step sequence with over 97% accuracy on the test set. The model was exported and used in an Android app. I had a lot of fun testing it on my phone, but it seems like more fine tuning (or changing the dataset) is required. Did you try the app? Can you improve it?

The source code for this part is available (including the Android app) on GitHub.