Towards interpretable and edge-intelligent masseter monitoring: a self-powered framework for on-device and continuous assessment

Continuous and interpretable monitoring of masseter muscle activity is essential for the assessment of sleep bruxism (SB) and temporomandibular dysfunction (TMD). However, existing surface electromyography (sEMG) systems remain constrained by wired power supply, data-privacy concerns, and limited real-time specificity. To address these gaps, this study introduces a self-powered, edge-intelligent monitoring framework that combines poly(vinylidene fluoride) (PVDF)-based piezoelectric patches (BP-Patch) with a dual-branch lightweight neural network, the Depthwise Separable Convolutional Network with Efficient Channel Attention (DSC-AttNet). The network leverages depthwise separable convolution (DSC) to balance computational load and feature resolution, and incorporates an Efficient Channel Attention (ECA) module to enhance the discriminability between lateralised activations. After 8-bit quantisation, DSC-AttNet is deployed on an Arm Cortex-M4 microcontroller (MCU) while occupying only 80.7 KiB Flash and 72.8 KiB RAM, enabling real-time on-device inference across five physiological states (left/right bruxism, left/right chewing, and resting) with 94.75% classification accuracy and 63.6 ms average latency on data from 12 subjects. To support trustworthy AI-driven decision-making, Gradient-weighted Class Activation Mapping (Grad-CAM) and attention-based relevance analysis are employed to identify class-specific activation patterns across both time and frequency domains. These interpretable features further enable the derivation of clinically relevant indices such as nightly bruxism count, episode duration, and the Masseter Symmetry Index (MSI). By integrating bilateral self-powered sensing, resource-efficient edge inference, and quantitative interpretability within a fully on-device framework, this work lays the groundwork for long-term, home-based assessment and privacy-preserving intervention in masseter monitoring.