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.
- eSense device data : There are 7 types of activities. The data is collected from 6 persons
- Jupyter Notebook
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)-
Activity Types for this dataset
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Confusion Matrix using CNN for the dataset
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Classification Report for the model
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