Human Activity Recognition from eSense datasets

eSense is an earable device. It has built-in sensors which provides accelerometer and gyroscope data. From this data, human activities are recognized using machine learning algorithm.

Dataset

• eSense device data : There are 7 types of activities. The data is collected from 6 persons

Tools

• Jupyter Notebook

1D CNN Model

model = Sequential()
model.add(Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(n_timesteps,n_features)))
model.add(Conv1D(filters=64, kernel_size=3, activation='relu', padding = 'same'))
model.add(Dropout(0.5))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(100, activation='relu'))
model.add(Dense(n_outputs, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    
# fit network
history = model.fit(X_train, y_train, validation_data=(X_test, y_test), 
                    epochs=epochs, batch_size=batch_size, verbose=verbose)

Description

• Activity Types for this dataset
  

• Confusion Matrix using CNN for the dataset
  

• Classification Report for the model