Innovative Earbuds Detect Drowsiness to Enhance Road Safety

Engineers at the University of California, Berkeley, have designed a prototype of earbuds that can detect signs of drowsiness in the brain. This innovative technology aims to protect drivers and machine operators from the dangers of drowsiness, a critical hazard contributing to road accidents worldwide. The earbuds function similarly to an electroencephalogram (EEG), a medical test used to evaluate the electrical activity in the brain.

They measure brain waves through built-in electrodes that make contact with the ear canal. While the electrical signals detected by these earbuds are smaller than those in traditional EEGs, the new study shows that the Ear EEG platform is sensitive enough to detect alpha waves. Alpha waves are a pattern of brain activity that increases when drowsiness sets in, making them a reliable indicator of a person’s alertness level.

The inspiration for this technology came to Rikky Muller, Associate Professor of Electrical Engineering and Computer Science at UC Berkeley, when he bought his first pair ofApple’sAirPods in 2017. I immediately thought what an amazing platform for neural recording, Muller said. He believes that this technology can classify drowsiness, indicating its potential to classify sleep and diagnose sleep disorders.

Creating an earbud that fits a variety of ear sizes and shapes posed significant challenges. While other groups used wet electrode gels or custom-moulded earpieces, Muller’s team aimed for a dry, user-generic model that anyone could use. Ryan Kaveh, the designer of the earbuds, said, My goal was to create a device usable every day by those who would benefit from it.

Kaveh designed the earpiece in three sizes. The design includes multiple electrodes applying gentle pressure to the ear canal, ensuring a comfortable fit. The signals are read through a low-power, wireless interface, making the device user-friendly and convenient.

In a paper, the researchers demonstrated the earpieces could detect physiological signals, including eye blinks and alpha brain waves. The study incorporated machine learning to validate the earpieces’ real-world application. Nine volunteers wore the earpieces while performing tasks in a darkened room, periodically rating their drowsiness and response times.

We found that even with seemingly lower signal quality, we could classify drowsiness onset as accurately as with more complex systems, Kaveh said. The earpieces’ accuracy in new users suggests they could work ‘out of the box’, without requiring any complex setup or calibration.

The team is also exploring other applications for the earbuds, including recording heartbeats, eye movements, and jaw clenches. This suggests that the technology could have broader applications beyond combating drowsiness, potentially contributing to various fields such as health monitoringand diagnostics.

The development of these earbuds represents a significant advancement in the field of wearable technology and its application in safety and health monitoring. By detecting signs of drowsiness, these earbuds could potentially save lives by preventing accidents caused by drowsy driving. Furthermore, their potential applications in diagnosing sleep disorders and monitoring other physiological signals suggest that this technology could have far-reaching implications in the field of health and wellness.