Biomechanical and machine learning integration for the detection of knee injuries: exploring the utility of non-intrusive elements

Joints play an essential role in the human body, allowing for movement, stability, and overall functionality. The knee, on the other hand, is a complex joint that contributes to the structural support of the body and enables a wide range of movements. Consequently, it is more susceptible to injury, which negatively impacts a person's daily routines. While there are important markers aiding in the assessment and diagnosis of joint health, they are typically evaluated using traditional qualitative methods. In this study, we focus on utilizing computational Machine Learning (ML) tools for recognizing patterns in injured joints. Specifically, three potential biomarkers were evaluated: joint sound, range of motion, and qualitative pain assessment. The purpose of this work is to explore the contribution of these three biomarkers in identifying knee injuries. A total of 22 participants, including both male and female individuals aged 23 and above, with and without a history of joint injury, were recorded and compared. A handheld device designed to measure the angular displacement and crackling sound of the joint was utilized. The tests consisted of 5 flexion and extension cycles, each comprising 4 repetitions. Signals were pre-processed, and 13 features were extracted for each frame. Using a binary classifier, dimensionality reduction methods, and signal processing by segmentation, the results showed an accuracy of 82\% in classifying the data with a precision of 83\%. The Machine Learning model successfully identified distinctive patterns between healthy and injured knees. Furthermore, the significant importance of considering the knee joint sound, presence of pain, and range of motion in clinical diagnostic evaluations to determine the presence or absence of a joint injury was highlighted.

https://orcid.org/0000-0002-5845-6663