A Wearable Knee Health Monitoring System Using Novel Flexible Piezoelectric Composite Films and Medical Knowledge-Guided Bayesian Machine Learning Diagnosis

Chronic knee conditions and musculoskeletal injuries are prevalent and pose significant challenges to global healthcare systems. Current diagnostic methods, such as MRI and X-ray, provide valuable insights but are limited by high costs and the inability to continuously monitor knee health. In response to these challenges, the proposed work makes the following contributions. Firstly, a novel wearable knee health monitoring system is developed, incorporating a flexible composite piezoelectric film that captures subtle patellar displacements as early indicators of knee joint abnormalities. Secondly, a Bayesian machine learning framework incorporating both expert knowledge and diagnostic data is proposed to address the potential limitations of current machine learning-based diagnostic methods, such as low accuracy and poor interpretability in medical practice. This framework integrates causal discovery algorithms, clinical insights, and medical expertise to identify the key relationships between machine learning models and medical principles, ultimately improving diagnostic accuracy. Finally, the proposed system achieves an 83.3% success rate in distinguishing between healthy, sub-healthy, and unhealthy knee states, outperforming traditional methods with an accuracy improvement of 6.4% to 17%. Furthermore, the system integrates a wireless sensor network and user-friendly software, enabling convenient monitoring and diagnosis in both clinical and home settings. The main innovation of this study lies in the design of a wearable sensor network structure based on patellar activity and a medical knowledge-guided Bayesian framework to enable a three-state classification diagnosis, namely, healthy, sub-healthy, and unhealthy. This system is particularly suited for knee health assessments in outpatient clinics, community settings, and home environments..