Research pick: Music and sleep - "Curtailing insomnia in a non-intrusive hardware less approach with machine learning"

Many people who suffer insomnia resort to listening to music through headphones to help them nod off. There is a problem with such a strategy in that once they do fall asleep they may well spend many hours with the music playing into their ears. This could lead to long-term, irreversible, hearing problems, such as deafness and tinnitus, especially if the insomniac prefers the music to be played at high volumes despite their need to go to sleep.

Research published in the International Journal of Medical Engineering and Informatics offers a non-invasive approach to detecting when someone has fallen asleep that would allow their device to automatically mute the music, perhaps with a gentle fadeout. The approach utilizes machine learning to determine whether the user has fallen asleep without the need for the user to wear a fitness tracker device. It simply works with the smartphone operating system Android. It thus does not require the insomniac user to purchase additional hardware.

Aside, from tiredness, unhealthy sleep patterns, such as insomnia, are a risk factor for mental and physical health problems. Music is a useful intervention for insomnia for many people, although the choice of music and its duration are critical if the approach is to be effective. The international team has incorporated a music recommendation system into their software which nudges the user towards a music form that has been demonstrated to be efficacious for inducing sleep, raga, an improvisational form in Indian classical music. The team suggests that they might also incorporate a feedback system that records which particular raga were most effective at quickly inducing sleep, and so feed this data to other users to improve the system.

Millions of people the world over struggle to fall asleep and suffer problems as a consequence. The new work offers a possible technological answer that does not require hardware intervention and could help avoid hearing problems associated with one of the most common interventions for insomnia, listening to music.

Vasudevan, S.K., Raguraman, T.B. and Pulari, S.R. (2022) ‘Curtailing insomnia in a non-intrusive hardware less approach with machine learning’, Int. J. Medical Engineering and Informatics, Vol. 14, No. 6, pp.537–549.