Classification model using RNN(action detection) 1) Could it be useful to use RNN for classification problem?(e.g.
to distinguish which action is taken: car is going, walking,
digging, nothing).
If 1 question is positive, how should RNN structure look like?


I have dataset of 4 actions, many examples for each action, each example includes 124 samples. 
So my X_train, X_test are arrays of float(400000, 124, 1); y_train, y_test are arrays of int(0 or 1 or 2 or 3 depends on action).

My data preprocessing:

X_train, X_test, y_train, y_test = np.array(X_train), np.array(X_test), np.array(y_train), np.array(y_test)
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1)) 
X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))


My structure:

regressor = Sequential()
regressor.add(LSTM(units=55, return_sequences=True, input_shape=(X_train.shape[1], 1)))
regressor.add(Dropout(0.2)) 
regressor.add(LSTM(units=50))
regressor.add(Dropout(0.2))
regressor.add(Dense(4,activation='softmax'))

regressor.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
regressor.fit(X_train, y_train, epochs=5, batch_size=32)
y_pred = regressor.predict(X_test)
@CapJS,

Instead of going ahead with an RNN, which helps you model the dependencies and relationship between your content, I would suggest you to take a look at 1D convolution networks to achieve the classification of activity. 

There is a nice post talking about something similar : https://machinelearningmastery.com/cnn-models-for-human-activity-recognition-time-series-classification/ 

With 1D convolution, you are by design gaining better processing performance and given that you have just 124 samples per activity, you do not need a deep layered network as the data size is still quite small. 

Given the data size, I would also suggest you to go ahead with something as simple as logistic regression, random forest approach. Hope this helps.