Artificial Intelligence Can Tell If You’re “High” Using Data On Your Smartphone

A study using artificial intelligence to analyze smartphone data successfully identified behaviors indicating cannabis intoxication with up to 90% accuracy. The technology discreetly utilizes various smartphone sensors, capturing over 100 different behavioral and environmental factors, and could potentially aid cannabis users in making safer decisions while under the influence of cannabis.

The latest research using artificial intelligence to predict cannabis intoxication based on biometric data from smartphone sensors is astonishing in its precision.

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AI Research for Detecting Cannabis Use

Researchers from the Stevens Institute of Technology recently published a study in the journal Drug and Alcohol Dependence, which analyzed data from the phones of cannabis users and non-users. Cannabis users independently reported consumption times and intoxication levels, classifying it on a simple scale of 1 to 10.

Comparing over 100 different factors, such as time, location, noise level, and movement recorded by the phones of both groups, scientists argue that they noticed significant differences when cannabis users were under its influence. These differences would be difficult to capture with ordinary human senses. Similar technology has been used before to study the effects of alcohol and other substances.

The AI Utilizes Smartphone Sensors

“Smartphones with mobile sensors are ubiquitous and can discreetly monitor our behavior,” says Sang Won Bae, an assistant professor at the Stevens Institute of Technology, leading the study. “They are not distracting, there’s no need to wear them, and the data collected by them can help avoid mistaken decisions under the influence of substances.”

Potential in Detecting Psychoactive Effects of Cannabis

The observed differences in the data were used to train a machine learning model. It is possible that in the future, it will allow for the identification of people under the influence of cannabis in real-time based on information from phone sensors. Possible interventions include notifications suggesting the use of transportation services. Researchers found that their AI model could predict cannabis intoxication with 90% accuracy after training on phone data.

“The goal is to predict people’s behavior to support them in times of physical or mental impairment,” says Bae.

Although the study claimed that it could predict cannabis intoxication based only on phone data with 67% accuracy, after correlating with temporal data (day of the week, time of day), accuracy increased to 90%. A scale from 0 to 10 was used to determine the level of intoxication.

“We tested the importance of temporal features compared only to data from smartphone sensors,” says the report, indicating that the AI model could predict intoxication with 60% accuracy based solely on temporal factors.

Limitations of the Artificial Intelligence Study

Despite promising results, the study had certain limitations, such as a small sample of participants or potential errors in cannabis users’ self-reporting.

This is not the first attempt to detect real-time cannabis intoxication. Most blood, saliva, or urine tests cannot predict whether a person is under the influence of cannabis; they only detect recent consumption. Companies are working on eye movement analysis technology to detect intoxication, but they have not yet been introduced to the market.

Conclusion

“This artificial intelligence study showed that using data from smartphone sensors to detect subjective cannabis intoxication in a natural environment among young adults is possible,” says the report.

However, the results are preliminary, and further artificial intelligence research is needed. The findings of this study are still in the initial phase, and broader-scale research is required for machine learning models to better understand the mechanisms characterizing people under the influence of cannabis.

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(Featured image by Amanda Dalbjörn via Unsplash)

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First published in Fakty Konopne, a third-party contributor translated and adapted the article from the original. In case of discrepancy, the original will prevail.

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