RISE: Adaptive Music Playback for Realtime Intensity Synchronization with Exercise

Abstract
We propose a system to adapt a user’s music to their exercise by aligning high-energy music segments with intense intervals of the workout. Listening to music during exercise can boost motivation and performance. However, the structure of the music may be different from the user’s natural phases of rest and work, causing users to rest longer than needed while waiting for a motivational section, or lose motivation mid-work if the section ends too soon. To address this, our system, called RISE, automatically estimates the intense segments in music and uses cutpoint-based music rearrangement techniques to dynamically extend and shorten different segments of the user’s song to fit the ongoing exercise routine. RISE supports both pre-planed adaptations for guided workouts and realtime adaptations for unguided sessions. Currently, exercise state is determined via manual input. We evaluated RISE with 12 participants who compared our system to a non-adaptive music baseline while exercising in our lab. Participants found our rearrangements seamless, intensity estimation accurate, and many recalled moments when intensity alignment helped them push through their workout.