Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Can a smartphone learn our eating habits without the user being in the loop? Clearly, the phone could use checkins based on location to infer that if you re in a cafe, for example, there is a good possibility you might eat or drink something. ,  use inferred behavioral data and location history to predict if you are going to eat or not in the near future. These predictors could serve as a basis for future eating trackers that work unobtrusively in the background of your phone rather than relying on burdensome user input. ,  report on a simple model that predicts the food purchases of a group of undergraduate college students (N=25) using inferred behavioral and location data from smartphones. The 10-ek study uses the dining related purchase records from student college cards as ground-truth to validate our prediction model. Initial results show that  can predict food and drink purchases with an accuracy of 74% using three eks of training data.
['Fanglin Chen', 'Rui Wang', 'Xia Zhou', 'Andrew T. Campbell']
I am writing a research paper on a idea My smartphone knows i am hungry
[{"from": "human", "value": "I am writing a research paper on a idea My smartphone knows i am hungry"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Can a smartphone learn our eating habits without the user being in the loop? Clearly, the phone could use checkins based on location to infer that if you re in a cafe, for example, there is a good possibility you might eat or drink something. ,  use inferred behavioral data and location history to predict if you are going to eat or not in the near future. These predictors could serve as a basis for future eating trackers that work unobtrusively in the background of your phone rather than relying on burdensome user input. ,  report on a simple model that predicts the food purchases of a group of undergraduate college students (N=25) using inferred behavioral and location data from smartphones. The 10-ek study uses the dining related purchase records from student college cards as ground-truth to validate our prediction model. Initial results show that  can predict food and drink purchases with an accuracy of 74% using three eks of training data."}]