Mobile blood alcohol content and impairment sensing device
OwnerEmmanuel Agu, Christina Aielloinventors
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Abstract
In a mobile sensing device, a method of detecting blood alcohol content includes receiving time-series gait data from at least one sensor of the mobile sensing device as a user walks and detecting a set of attributes associated with the time-series gait data, each attribute of the set of attributes related to the user's gait. The method includes comparing the set of attributes with a machine learning classification model learned from a training data set of attributes to determine at least one of a blood alcohol content range of the user and an impairment level of the user and outputting a notification associated with the at least one of the blood alcohol content range of the user and the impairment level of the user.
Claims17
In a mobile sensing device, a method of detecting blood alcohol content, comprising:
- receiving, by the mobile sensing device, time-series gait data from at least one sensor of the mobile sensing device as a user walks;
- detecting, by the mobile sensing device, a set of attributes associated with the time-series gait data, each attribute of the set of attributes related to the user's gait;
- comparing, by the mobile sensing device, the set of attributes with a machine learning classification model learned from a training data set of attributes to determine at least one of a blood alcohol content range of the user and an impairment level of the user;
- and
- outputting, by the mobile sensing device, a notification associated with the at least one of the blood alcohol content range of the user and the impairment level of the user;
- wherein comparing the set of attributes with the machine learning classification model comprises:
- combining, by the mobile sensing device, each attribute of the set of attributes and a corresponding baseline attribute associated with baseline time-series gait data to generate a set of normalized attributes independent from a baseline motion of the user, and
- comparing, by the mobile sensing device, each attribute of the set of normalized attributes with the machine learning classification model to determine the at least one of the blood alcohol content range of the user and the impairment level of the user.
A mobile sensing device, comprising:
- at least one sensor;
- and
- a controller having a processor and a memory, the controller disposed in electrical communication with the at least one sensor, the controller configured to:
- receive time-series gait data from at least one sensor of the mobile sensing device as a user walks;
- detect a set of attributes associated with the time-series gait data, each attribute of the set of attributes related to the user's gait;
- compare the set of attributes with a machine learning classification model learned from a training data set of attributes to determine at least one of a blood alcohol content range of the user and an impairment level of the user;
- and
- output a notification associated with the at least one of the blood alcohol content range of the user and the impairment level of the user;
- wherein when comparing the set of attributes with the machine learning classification model, the controller is configured to:
- combine each attribute of the set of attributes and a corresponding baseline attribute associated with baseline time-series gait data to generate a set of normalized attributes independent from a baseline motion of the user, and
- compare each attribute of the set of normalized attributes with the machine learning classification model to determine the at least one of the blood alcohol content range of the user and the impairment level of the user.
A computer program product stored on a computer readable medium that when executed by a controller of a mobile sensing device configured the mobile sensing device to:
- receive time-series gait data from at least one sensor of the mobile sensing device as a user walks;
- detect a set of attributes associated with the time-series gait data, each attribute of the set of attributes related to the user's gait;
- combine each attribute of the set of attributes and a corresponding baseline attribute associated with baseline time-series gait data to generate a set of normalized attributes independent from a baseline motion of the user;
- and
- compare each attribute of the set of normalized attributes with a machine learning classification model learned from a training data set of attributes to determine at least one of a blood alcohol content range of the user and an impairment level of the user;
- and
- output a notification associated with the at least one of the blood alcohol content range of the user and the impairment level of the user.
Dependencies are inferred from the claim text.