AUTOENCODE-KNEE: Automatic feature extraction using CNN-based Autoencoder from Time-Frequency Distribution of Knee Joint

Background: This paper presents AUTOENCODE-KNEE, a novel approach for automatic feature extraction from the time-frequency distribution of knee joint signals. Knee joint signals often contain valuable information crucial for diagnosing various musculoskeletal disorders. However, manually extracting relevant features from these signals can be time-consuming and subjective. Method: To address this challenge, we propose utilizing a convolutional neural network (CNN)-based autoencoder architecture for automatic feature extraction. The autoencoder is trained on a dataset comprising time-frequency representations of knee joint signals, learning to encode and decode the input signals while preserving important features. By leveraging the inherent ability of CNNs to capture spatial dependencies, the autoencoder effectively learns to extract discriminative features from the complex time-frequency domain. Result: Our experimental results demonstrate the efficacy of AUTOENCODE-KNEE in automatically extracting meaningful features from knee joint signals. We evaluate the extracted features on various classification tasks related to musculoskeletal disorder diagnosis, showcasing the utility of the proposed approach in aiding healthcare professionals in accurate and efficient diagnosis. Conclusion: In summary, AUTOENCODE-KNEE offers a promising solution for automatic feature extraction from knee joint signals, potentially revolutionizing how musculoskeletal disorders are diagnosed and treated.