  We present GrooveMeter, a novel system that automatically detects vocal and
motion reactions to music via earable sensing and supports music
engagement-aware applications. To this end, we use smart earbuds as sensing
devices, which are already widely used for music listening, and devise reaction
detection techniques by leveraging an inertial measurement unit (IMU) and a
microphone on earbuds. To explore reactions in daily music-listening
situations, we collect the first kind of dataset, MusicReactionSet, containing
926-minute-long IMU and audio data with 30 participants. With the dataset, we
discover a set of unique challenges in detecting music listening reactions
accurately and robustly using audio and motion sensing. We devise sophisticated
processing pipelines to make reaction detection accurate and efficient. We
present a comprehensive evaluation to examine the performance of reaction
detection and system cost. It shows that GrooveMeter achieves the macro F1
scores of 0.89 for vocal reaction and 0.81 for motion reaction with
leave-one-subject-out cross-validation. More importantly, GrooveMeter shows
higher accuracy and robustness compared to alternative methods. We also show
that our filtering approach reduces 50% or more of the energy overhead.
Finally, we demonstrate the potential use cases through a case study.
