IMJP-Net: inertial–music joint prediction for emotion-aware recommendation via smart-glasses sensing

Emotion fluctuations during music listening are closely tied to a listener’s current emotional state, yet most recommendation systems rely on historical behavior data, which struggle with cold-start issues and real-time adaptability. In this work, we propose IMJP-Net, a framework that leverages smart glasses IMU signals as a privacy-preserving implicit feedback channel for emotion-aware music recommendation. To resolve the non-stationarity of head-worn dynamics, the system integrates an IMU Evolution Module (IEM) for temporal inertial encoding and an IMU-Music Mutual Attentive Alignment (IMMAA) mechanism for cross-modal feature fusion. Evaluated on 1164 valid trials (), IMJP-Net achieves 19.6% improvement over baselines in joint prediction of music preference and post-listening emotional states. Ablation studies demonstrate that excluding IMU signals results in a 37.7% increase in mean MAE, confirming the critical contribution of inertial sensing to real-time affect inference. An in-the-wild user study () validates practical utility, with participants highlighting enhanced emotional resonance, as well as privacy and non-intrusiveness. These results demonstrate the potential of IMJP-Net for pervasive emotion-aware music recommendation.