A Spectrogram Based Learning Approach for Knee Joint Analysis

Human joints, particularly the knees and hips, produce acoustic emissions during movement that reflect both joint type and health condition. This paper introduces SpectroNet, an innovative deep learning methodology that uses two-dimensional spectrograms transformed by joint acoustic emissions for enhanced classification of knee joint health conditions. By leveraging spectrograms as input representations, SpectroNet effectively captures subtle acoustic signatures indicative of joint pathology while eliminating the need for extensive preprocessing. Our comparative evaluation demonstrates that SpectroNet achieves superior performance compared to traditional machine learning techniques when classifying healthy versus inflammatory knee conditions.