I have a dataset collected in a smart home and I would like to do Activity Recognition. I am used to classification but in this case data comes from 4 different sources:
- Accelerometer from wrist
- Binary sensors around the house
- proximity beacons (to understand which room the user is at any moment)
- ground pressure from floor
Data is labelled and has 25 activities.
At the moment I am working with a CNN on the activity images coming from the sensors and I can reach 45% accuracy, but I need to add the other sources to achieve better results.
- how to use all (or a group of) different sources for example in a NN?
- Can I train 4 different classifier that works together to make a prediction?
- Maybe some classifiers try to understand simpler activities and other try to find more complex activities?