Musical app that listens to your heart during exercise

Researchers have designed a new “Musical Heart” that enables your smartphone to select music to get your heart pumping during workout.

The biofeedback-based system developed by a University of Virginia graduate researcher helps smartphones select music that will help get their owners’ heart pumping during exercise and slow it down when they want to cool down or relax.

“Whether I am driving, jogging, travelling or relaxing - I never find the appropriate music to listen to,” said Shahriar Nirjon, a doctoral student in computer science in the School of Engineering and Applied Science.

“I believe there are many like me. The problem is: The heart wants to hear something, but our music player does not understand the need. My joy was in connecting them together – in a non-invasive and cost-effective way,” Nirjon said.

Called “Musical Heart”, the system “brings together wellness and entertainment,” Nirjon said in a statement.

Musical Heart works by merging a microphone that detects the pulse in arteries in the ear with earphones that bring in music from a playlist on a smartphone.

An app selects tunes that optimise the heart rate of an individual user based on a given activity, whether running, walking or relaxing - playing fast-paced music for hard workouts, and slowing the beat for cool-downs.

An algorithm refines the music selection process of the system by storing heart rate data and calculating the effects of selected music on the rate. Over time, it improves music selections to optimise the user’s heart rate.

“We’ve designed Musical Heart to be convenient, non-invasive, personalised and low cost,” Nirjon said. Musical Heart uses a pair of specially built earphones equipped with tiny sensors to continuously monitor the user’s heart rate and activity level.

That physiological information, along with contextual information, is then sent to a remote server, which provides dynamic music suggestions to help the user maintain a target heart rate.

The system learns, in essence, to select music that will have a desired effect on heart rate customised to the individual user, based on the effects of past music selections on the heart rate.