Electrical Engineering and Computer Science Student Publications
Document Type
Conference Proceeding
Publication Title
The International FLAIRS Conference Proceedings
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 (30x 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. 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.
DOI
10.32473/flairs.39.1.141783
Publication Date
2026
Recommended Citation
Syed, Muntaser; Sjöqvist, Alfred; Sedlik, Noah; and Liu, Tsing, "Real-Time Neck Posture Classification Using a Lightweight Wearable IMU Pendant" (2026). Electrical Engineering and Computer Science Student Publications. 105.
https://repository.fit.edu/ces_student/105