New Algorithm on Mobile Phones Detects Alcohol Poisoning Using Voice
Despite numerous awareness campaigns about the dangers of driving under the influence of alcohol, it remains one of the leading causes of traffic accidents. To address this issue, scientists have spent years searching for new methods to measure alcohol metabolism in the body. While breathalyzers used at roadblocks are effective, they are expensive and uncomfortable. In light of this, a team of researchers from Stanford and Toronto Universities has developed a mobile phone algorithm that can detect alcohol poisoning simply by recording the user’s voice.
The decision to focus on mobile phones as a detection tool was made due to their widespread ownership. Almost everyone possesses a mobile phone nowadays, making it convenient to install the necessary app. Additionally, this method allows for regular alcohol measurements without requiring the user to manually indicate their alcohol consumption. The mobile phone would periodically analyze voice recordings from the user’s daily conversations. If dangerous levels of alcohol poisoning are detected, an alarm would be triggered.
What sets this algorithm apart is its potential to be applied to other parameters, such as walking cadence or text messaging patterns. By combining multiple parameters, an even more comprehensive analysis can be achieved. However, the researchers understand that it is crucial to take a step-by-step approach. Currently, the focus is on testing the algorithm’s effectiveness in detecting alcohol poisoning through voice recordings, which has already yielded impressive results.
Anyone who has experienced the effects of excessive alcohol consumption knows that a slurred voice and impaired speech are among the first noticeable signs. However, there may also be additional imperceptible parameters that are common during intoxication.
To address this, the scientists utilized machine learning and artificial intelligence (AI) to design an algorithm capable of detecting voice patterns associated with alcohol poisoning. The study involved 18 participants over the age of 21, who were given a controlled dose of alcohol. Prior to consuming alcohol, breath samples were collected, and the participants were asked to read a tongue twister aloud. This process was repeated every hour for a duration of seven hours, with breath samples taken every half hour.
Through this experiment, the algorithm successfully identified changes in the voice that corresponded to the level of alcohol detected in the exhaled breath samples. After training the AI, the algorithm demonstrated a remarkable 98% accuracy in measuring alcohol levels through voice recordings.
It is important to note that this study had a small sample size with limited participants. The researchers acknowledge the need for more extensive experiments involving diverse volunteers, including individuals from different ethnic backgrounds. For instance, it is well-known that Asian individuals often have lower tolerance for alcohol due to the deficiency of a specific protein required for its metabolism. Therefore, this aspect should be considered in future studies.
In addition, the researchers emphasize the significance of considering other factors. Waiting for an intoxicated person to take out their phone, activate the app, and record themselves reading a tongue twister may not be practical. As a result, the application should have the capability to work automatically and take periodic measurements. In the case of voice detection, it should be able to distinguish changes in speech amidst background noise. Furthermore, if the user is in a noisy environment, alternative parameters like walking patterns could be employed. Similarly, if the user remains stationary at a bar counter, their text composition could provide valuable insights. Ultimately, an algorithm trained to detect anomalies across various parameters and determine the appropriate ones to utilize in each situation would be ideal.
In conclusion, the development of an algorithm capable of detecting alcohol poisoning through voice recordings on mobile phones is a significant breakthrough. This technology offers a non-invasive, cost-effective approach that can help prevent traffic accidents caused by alcohol impairment. However, further research with larger and diverse participant groups is necessary for a comprehensive understanding of the algorithm’s effectiveness. Moreover, considering additional factors and integrating multiple parameters will enhance its applicability. By combining machine learning, AI, and mobile technology, this innovation has the potential to save lives and improve public safety.
