Emteq organized Human Activty Recognition Challenge 2019. The main theme of this challenge is to recognize different types of activities from sensor data using any machine learning model.
- There are 4 persons' data. 3 persons' data is used as train data and 4th person's data is used as test data
- Dataset download Link : Emteq Dataset
- 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'))
- 2nd prize was won from Sozo Lab team. Result