Real-Time Neck Posture Classification Using a Lightweight Wearable IMU Pendant

Abstract

Poor neck posture during prolonged device use contributes to musculoskeletal disorders affecting millions worldwide. Existing posture monitoring solutions rely on camera-based systems or complex multi-sensor arrays, limiting their practicality
for continuous daily use. We present a lightweight, chest-worn pendant using a single 6-axis IMU (accelerometer and gyroscope) for real-time classification of seven neck posture states: neutral, mild flexion, moderate flexion, severe flexion, extension, lateral tilt, and lying. Our approach employs an ensemble architecture combining bidirectional LSTM, Transformer encoder, and 1D-CNN models with learnable fusion weights. To address limited training data, we apply aggressive data augmentation (30× multiplication) including noise injection, magnitude scaling, time warping, and rotation simulation. We further propose a hybrid classification strategy that
fuses deep learning predictions with physics-based threshold rules derived from accelerometer orientation. Evaluation with 10 subjects using leave-one-subject-out (LOSO) cross-validation achieved 96.5% mean accuracy with the BiLSTM backbone and 95.0% with the full ensemble. An architecture ablation reveals that the BiLSTM alone outperforms the ensemble, suggesting a lighter model suffices for this task. The complete system runs on an Armv8-M STAR-MC1 microcontroller (480MHz) with a 1.75-inch AMOLED touch display, providing visual feedback and haptic alerts when poor posture is sustained beyond a configurable threshold. Our results demonstrate that accurate posture monitoring is achievable with minimal, unobtrusive hardware suitable for everyday wearable use.