Multimodal EMG–IMU sensor fusion with dual-output LSTM for fatigue estimation during neonatal chest compressions

• Multimodal fusion of EMG physiology and IMU biomechanics to estimate rescuer fatigue during neonatal CPR. • Dual-output LSTM jointly classifies fatigue level and predicts fatigue-onset time. • Fusion significantly outperforms EMG-only and IMU-only approaches (Acc 98.3%, AUC 0.99; onset RMSE 38.3 s). • Subject-exclusive validation demonstrates generalization; design meets real-time constraints for wearable feedback. During neonatal cardiopulmonary resuscitation (CPR), rescuer fatigue develops rapidly and compromises compression quality. Conventional feedback systems infer fatigue indirectly from mechanics (depth/rate) and may miss early neuromuscular changes. To develop and evaluate a multimodal framework that fuses surface EMG (physiology) and IMU (biomechanics) to improve the accuracy of (i) fatigue level classification and (ii) prediction of fatigue-onset time during neonatal chest compressions (NCPR). Twenty trained providers performed simulated neonatal compressions on a manikin while synchronized EMG (deltoid, triceps, upper trapezius) and 3-axis IMU signals were recorded and windowed (2 s, 50% overlap). Features included EMG RMS, MAV, median frequency (MF), and IMU depth dynamics. A dual-output LSTM jointly produced 3-class fatigue labels and onset-time regression. Fusion outperformed unimodal models: 98.3% accuracy, macro-F1 0.982, AUC 0.99; onset prediction RMSE 38.3 s, R² 0.68. EMG-only: 69.4% accuracy; IMU-only: 96.7%. EMG provided early physiological fatigue signatures, complementing IMU mechanical degradation. EMG–IMU fusion with temporal deep learning improves fatigue estimation during NCPR and is suitable for real-time feedback to support optimal rescuer rotation. Earlier, physiology-aware fatigue detection enables proactive team management before compression quality declines. Lightweight LSTM fusion runs in real time and generalizes across rescuers.