A system may autonomously monitor the blood alcohol content (BAC) and/or degree of intoxication of a user by using a machine learning module. The machine learning module may include a machine learning classification model trained on data relating to the user. The system may collect data relating to the user about a first set of characteristics, such as heart rate, body temperature, physical coordination, physical appearance, and speech quality. The machine learning classification model may receive the data about the first set of characteristics to determine a first BAC and/or degree of intoxication range of the user. If the machine learning classification model determines that the first BAC range is less than a threshold, the system may output a notification associated with the first BAC range. If the machine learning classification model determines that the first BAC range equals or exceeds the threshold, the system may collect data relating to the user about a second set of characteristics, such as amount of alcohol consumption, physical responsiveness, and cognitive responsiveness. The machine learning classification model may then receive data about the second set of characteristics to determine a second BAC range and/or degree of intoxication and output a notification associated with the second BAC range.
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Lee, Yun Sun; Brantingham, Luke; Kandibanda, Aaditya; and Murugan, Kavinaath, "AUTONOMOUS MONITORING OF BLOOD ALCOHOL CONTENT", Technical Disclosure Commons, (December 14, 2020)