Do you remember when your parents tried to convince you as a child to eat vegetables by telling you that were they good for your health? It’s the same tactic many apparel manufacturers are using today when adding health features to their devices.. And following this line, a group of researchers have developed a system so that the headset also controls the t’s healthor heard.
Every time Apple makes one of its famous keynotestake a few minutes to promote the benefits of the health you have use one of their Apple Watch, like your heart rate sensor is able to identify some heart problems before aging. It is also said that the new generation of their AirPods Pro could include a sensor to measure body temperaturewhich would allow these the devices detect if the user has fever, an early symptom of many Other terms.
but rIt turns out that the very ability of the headphones for send sound to your ears could also be used for detect ailments that I cann affect the inner ear and ear canal, as researchers at the University at Buffalo found an experimental device they developed called EarHealth.
The coolest thing about EarHealth is that its hardware is basically that of a normal headset, but with an upgraded microphone inside designed to pick up sounds in the ear. Although in the shared images of EarHealth wired headphones are seen, researchers say their invention is also applicable Some Bluetooth headphones.
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Mientras que el Apple Watch usa su detección óptica para controlar tu ritmo cardíaco, los EarHealth utilizan sonidos. Los auriculares emiten un rápido chirrido que reverbera a través de tu canal auditivo, produciendo sonidos y ecos únicos que captura el micrófono. Esos sonidos capturados luego son procesados por una aplicación de tu smartphone que utiliza algoritmos de deep learning para generar un perfil con la geometría del oído interno del usuario.
La primera medición tiene que realizarse cuando el usuario está sano, de forma que se pueda crear un perfil de referencia de su oído interno, mientras que el resto de mediciones, que se pueden programar regularmente, generan perfiles que se comparan con el original para detectar diferencias. Estas mediciones pueden utilizarse para diagnosticar tres cosas diferentes: una obstrucción, un tímpano roto o una otitis media (una infección o inflamación común del oído medio).
En las pruebas realizadas sobre 92 usuarios (que incluían 27 sujetos sanos, 22 con un tímpano roto, 25 con otitis media y 18 con obstrucciones), EarHealth acertó en el 82,6% de los diagnósticos, pero es una cantidad que solo puede ir a mejor a medida que los investigadores vayan refinando el hardware y adquiriendo más muestras de usuarios. El beneficio de usar algoritmos de inteligencia artificial es que que se volverán más precisos con el tiempo a medida que haya más datos disponibles.