2H-MoS2 lubrication-enhanced MWCNT nanocomposite for subtle bio-motion piezoresistive detection with deep learning integration

• A nacre-like MWCNT/2H-MoS 2 nanocomposite fabricated via a low-cost, eco-friendly vacuum filtration process. • First validation of the dual enhancement effect of MoS 2 in both gauge performance and mechanical robustness. • Consistent and high-fidelity subtle-to-moderate biophysical signal detection. • Achieved 98% accuracy in classifying swallowing activities per international protocol using a fine-tuned InceptionTime model. Intelligent piezoresistive health monitoring systems integrate advanced nanocomposite architectures with precise algorithmic analysis for real-time physiological assessment. However, existing works often prioritize high sensitivity at the expense of strain tolerance and require complex fabrication procedures. Herein, we present an environmentally friendly, low-cost, and nonionic fabrication approach for a 2H-phase molybdenum disulfide (2H-MoS 2 )-enhanced multi-walled carbon nanotube (MWCNT) strain sensor, developed via a systematically optimized vacuum-assisted filtration process. This study is the first to validate the dual enhancement effect of MoS 2 , leveraging its shear-exfoliation properties to simultaneously improve strain gauge performance and mechanical robustness. The resulting nacre-like layered hybrid nanocomposite achieves a remarkable gauge factor of 675.7 (R 2 ∼0.993) at low strain (∼0–4.5 %), representing a 3881.5 % improvement over pure MWCNT systems, alongside enhanced toughness (∼89.17 %) and strain tolerance (∼53.93 %). Meanwhile, the optimized composition ensures low rest-state resistance (∼13.1 Ω), minimal hysteresis (∼5.7 %), and robust durability over 5000 cycles at 10 % strain. As a result, the proposed sensor enables highly consistent, high-fidelity monitoring of various subtle-to-moderate biomotions. Integrated with a fine-tuned InceptionTime deep learning model, it achieves an F1-score of 98 % in classifying Dysphagia Diet Standardization Initiative (IDDSI)-standard swallowing activities, demonstrating its potential for AI-driven health monitoring applications.