Uncertainty-aware posture quality assessment with Bayesian liquid neural networks

Automated posture quality assessment has become increasingly important in health and wellness technology. However, existing methods predominantly provide point estimates without quantifying prediction confidence, posing a critical limitation in safety-sensitive applications where incorrect assessments may lead to improper guidance. This paper addresses this gap by introducing a reliability-oriented evaluation framework based on Bayesian Liquid Neural Networks (LNNBI). Unlike conventional approaches, our method simultaneously delivers a quality score and a principled uncertainty measure derived from Bayesian variational inference. More importantly, we incorporate a rejection mechanism that enables the model to abstain from making predictions when uncertainty exceeds a predefined threshold, establishing a built-in safeguard for practical deployment. To support this framework, we construct a longitudinal dataset comprising 50 participants over 200 sessions, decomposing four fundamental human postures into seven biomechanical indicators. Experimental results demonstrate that LNNBI achieves 0.8715 accuracy and 0.9105 precision, outperforming LSTM, MC-GRU, TSGCN, and YoNet baselines. Uncertainty analysis reveals a strong correlation between prediction error and uncertainty (Pearson's r = 0.73), confirming that the model reliably identifies its own low-confidence predictions. With a 15% rejection rate, the effective accuracy reaches 0.923. Correlation analysis further identifies Pelvic Position and Neutrality (r = 0.682) and Spinal Alignment (r = 0.595) as the most influential biomechanical indicators. This work offers a reliability-first perspective on automated posture assessment, with promising potential for deployment in personalized health coaching and remote guidance, particularly on GPU-accelerated systems.