Here is the scenario: There are three chefs(A- main chef, B and C- assistant) working together to prepare a diner set. The sequence of the event is as following.
- Start: The three chefs enter the kitchen together with the ingredients.
- Preparation: They prepare the utensils and the ingredients(e.g cleaning and cutting).
- Appetizer is ready to be served.
- Main dish is ready to be served
- Dessert is ready to be served.
- End: All the chefs leave the kitchen together.
My question: What machine-learning method can I use to predict the event at any point of time? Let say I can use sensors or image/video processing to collect data, what kind of data should I gather for the suggested method?
Edit: Sorry for the unclear question. What I have in mind now is, I put some sensors(or camera) to detect the start of the event with a timestamp at: the entrance, the washing corner, the cutting corner, the appetizer corner, the cooking stove(for main dish), the oven(for dessert), and the serving counter. So I have these sensor events:
- Start time (chefs enter the kitchen)
- Cleaning start time (ingredients preparation)
- Cutting start time (ingredients preparation)
- Appetizer start time
- Stove start time (for main dish)
- Oven start time (for dessert)
- Serving counter (the time when one of the menu is ready to be served)
- End time (chefs leave the kitchen)
I collect the dataset from 20 cooking cases. Applying learning algorithm to do event prediction. For example, there's a cooking started at 8:15am, cutting started at 8:45am, based on the learned data, I want to predict when will the appetizer be ready.