Systems and methods for predicting real-time behavioral risks using everyday images

A method comprising:

• receiving, from a camera, one or more images representative of a location;
• applying a trained classifier to the one or more images to classify the location into one of at least two risk categories,
• wherein the classification is based on a likelihood of a subject performing a target behavior based on presence of the subject in the location,
• wherein the target behavior comprises smoking and wherein the trained classifier comprises a convolutional neural network trained to identify one or more objects in the one or more images, and
• wherein the trained classifier differentiates between a first risk category indicative of a smoking environment and a second risk category indicative of a nonsmoking environment with an accuracy greater than 70%;
• and
• issuing a risk alert responsive to the trained classifier classifying the location into a high-risk category, wherein the risk alert comprises one or more of an audible alert, a visual alert, or a tactile alert.

A mobile computing device comprising:

• a camera configured to generate one or more images representative of a location of the mobile computing device;
• and
• a non-transitory computer-readable medium comprising program instructions executable by at least one processor to cause the at least one processor to:
• apply a trained classifier to the one or more images to classify the location into one of at least two risk categories,
• wherein the classification is based on a likelihood of a subject performing a target behavior based on presence of the subject in the location,
• wherein the target behavior comprises smoking and wherein the trained classifier comprises a convolutional neural network trained to identify one or more objects in the one or more images, and
• wherein the trained classifier differentiates between a first risk category indicative of a smoking environment and a second risk category indicative of a nonsmoking environment with an accuracy greater than 70%;
• and
• issue a risk alert responsive to the trained classifier classifying the location into a high-risk category, wherein the risk alert comprises one or more of an audible alert, a visual alert, or a tactile alert.