Neural Network Sensordata as Input

Question

I have a dataset consisting of sensor recordings about human movement. There are 22 classes of different movement like sitting or walking and 19 sensor values.

Each recording of a movement has about 1000 lines contained in a csv file.

My problem: I don't know how to present those recordings to a neural network (TensorFlow) so that it can be trained on the movement classes and even predict what was done in recording by getting those 19000 values.
I don't even know which Neural Network Model I should use and therefore need your help.

1b15 · Answer

Correct me if I'm wrong: As far as I understood, you have files of 1000 entries with 19 numerical sensor values and a type of 22 movements in each line.
You would like to predict the type of movement based on the sensor values.

Are the entries being recorded in a fixed interval?
If so, you could use an RNN.

Either way, it seems to be a classification problem. You can find a tutorial for Tensorflow here.

You can read data from csv files with pandas.read_csv.

As for the encoding of your data: If the types of movement cannot be sorted and are not related to each other, you should use one-hot encoding to present the type of movement to the NN. You can keep numerical sensor data as is, just make sure to normalize it.

Neural Network Sensordata as Input

One Answer

Add your own answers!

Ask a Question