Systems and methods for food analysis, feedback, and recommendation using a sensor system
Systems and methods for analyzing food are disclosed. A first sensor captures first sensor data. A processor determines, based on the first sensor data, whether food has been detected, and invokes a second sensor in response to the determining. The second sensor captures second sensor data. The processor predicts, based on the first and second sensor data, a characteristic of the food. The processor further predicts a body's response to the food based on the characteristic, and outputs a recommendation based on the predicting of the body's response.
1 . A method for analyzing food comprising:
training a machine learning model based on training data including food data and a reaction of a user to the food data;
capturing, by a first sensor, first sensor data of a food item, wherein the first sensor data includes an image of the food item;
determining, by a processor, based on the first sensor data, whether food has been detected,
wherein the determining includes extracting one or more features of the food item, and identifying a first name of the food item based on the extracted features;
invoking, by the processor, a second sensor;
capturing, by the second sensor, second sensor data of the food item;
predicting, by the processor, based on the first and second sensor data, a characteristic of the food item, wherein the predicting includes:
identifying, by the processor, a second name for the food item based on the second sensor data, wherein the second name is different from the first name;
comparing, by the processor, the first name with the second name;
based on the comparing of the first name with the second name;
determining an inconsistency between the first name and the second name;
determining a first ranking associated with the first sensor and a second ranking associated with the second sensor;
determining that the second ranking is higher than the first ranking;
selecting, by the processor, the second name for the food item based on the second ranking being higher than the first ranking; and
predicting, by the processor, the characteristic of the food item based on the second name;
receiving, by the processor, from a third sensor, a body measurement of a user;
predicting, by the processor, a body's response to the food item based on the characteristic, wherein the predicting includes:
providing to the machine learning model, as input data, the characteristic of the food item and the body measurement; and
receiving from the machine learning model, a prediction of a change of the body measurement of the user based on the input data; and
outputting, by the processor, a recommendation based on the predicting of the body's response.
2 . The method of claim 1 , wherein the first sensor is a two-dimensional (2D) camera, and the first sensor data includes a two-dimensional image of the food captured by the 2D camera, the method further comprising:
extracting one or more features from the image; and
identifying the second name based on the one or more features.
3 . The method of claim 2 , wherein the second sensor is a three-dimensional (3D) camera, and the second sensor data includes depth information of the food captured by the 3D camera, the method further comprising:
computing volume of the food based on the depth information.
4 . The method of claim 2 , wherein the second sensor is a polarization camera, and the second sensor data includes a polarization image of the food captured by the polarization camera, the method further comprising:
determining, by the processor, molecular arrangement of the food based on the polarization image.
5 . The method of claim 2 , wherein the second sensor is a polarization camera, and the second sensor data includes a polarization image of the food item captured by the polarization camera, the method further comprising:
determining, by the processor, freshness of the food item based on the polarization image.
6 . The method of claim 2 , wherein the second sensor is a spectrometer, and the second sensor data includes wavelength of light absorption or transmittance, the method further comprising:
determining, by the processor, a molecular component of the food item based on the wavelength.
7 . The method of claim 1 , wherein the characteristic of the food item includes at least one of food nutrient or food volume.
8 . The method of claim 1 , wherein the predicting the characteristic of the food item includes:
validating the first name based on the second sensor data.
9 . The method of claim 1 , wherein the predicting the body's response to the food item includes:
invoking the machine learning model for predicting a level of at least one of glucose, triglycerides, or cholesterol, based on the characteristic of the food.
10 . The method of claim 1 , wherein the recommendation includes modifying an aspect of the food item.
11 . A system for analyzing food comprising:
a first sensor for capturing first sensor data of a food item, wherein the first sensor data includes an image of the food item;
a second sensor for capturing second sensor data;
a third sensor for capturing a body measurement of a user; and
a processing system coupled to the first sensor and the second sensor, the processing system comprising a processor and memory storing instructions that, when executed by the processor, cause the processor to perform:
training a machine learning model based on training data including food data and a reaction of a user to the food data;
receiving, from the first sensor, the first sensor data including the image of the food item;
determining, based on the first sensor data, whether food has been detected, wherein the determining includes extracting one or more features of the food item, and identifying a first name of the food item based on the extracted features;
invoking the second sensor;
receiving, from the second sensor, second sensor data;
predicting, based on the first and second sensor data, a characteristic of the food item, wherein the predicting includes:
identifying a second name for the food item based on the second sensor data, wherein the second name is different from the first name;
comparing the first name with the second name;
based on the comparing of the first name with the second name:
determining an inconsistency between the first name and the second name;
determining a first ranking associated with the first sensor and a second ranking associated with the second sensor;
determining that the second ranking is higher than the first ranking;
selecting the second name for the food item based on the comparing second ranking being higher than the first ranking; and
predicting the characteristic of the food item based on the second name;
receiving, from the third sensor, the body measurement of the user;
predicting a body's response to the food item based on the characteristic, wherein the predicting includes:
providing to the machine learning model, as input data, the characteristic of the food item and the body measurement; and
receiving from the machine learning model, a prediction of a change of the body measurement of the user based on the input data; and
outputting a recommendation based on the predicting of the body's response.
12 . The system of claim 11 , wherein the first sensor is a two-dimensional (2D) camera, and the first sensor data includes a two-dimensional image of the food captured by the 2D camera, wherein the instructions further cause the processor to:
extract one or more features from the image; and
identify a second name based on the one or more features.
13 . The system of claim 12 , wherein the second sensor is a three-dimensional (3D) camera, and the second sensor data includes depth information of the food captured by the 3D camera, wherein the instructions further cause the processor to:
compute volume of the food based on the depth information.
14 . The system of claim 12 , wherein the second sensor is a polarization camera, and the second sensor data includes a polarization image of the food captured by the polarization camera, wherein the instructions further cause the processor to:
determine molecular arrangement of the food based on the polarization image.
15 . The system of claim 12 , wherein the second sensor is a polarization camera, and the second sensor data includes a polarization image of the food item captured by the polarization camera, wherein the instructions further cause the processor to:
determine freshness of the food item based on the polarization image.
16 . The system of claim 12 , wherein the second sensor is a spectrometer, and the second sensor data includes wavelength of light absorption or transmittance, wherein the instructions further cause the processor to:
determine a molecular component of the food item based on the wavelength.
17 . The system of claim 11 , wherein the characteristic of the food item includes at least one of food nutrient or food volume.
18 . The system of claim 11 , wherein the instructions that cause the processor to predict the characteristic of the food item include instructions that cause the processor to:
validate the first name based on the second sensor data.
19 . The system of claim 11 , wherein the instructions that cause the processor to predict the body's response to the food item include instructions that cause the processor to:
invoke a machine learning model for predicting a level of at least one of glucose, triglycerides, or cholesterol, based on the characteristic of the food.
20 . The system of claim 11 , wherein the instructions that cause the processor to output a recommendation include instructions that cause the processor to recommend modifying an aspect of the food item.