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Knee is the most intricate joints in the body. This joint faces immense reaction forces during daily routine work that may vary around three to seven times of the body weight. These high reaction forces may convert small malfunctioning into severe conditions and can be avoided by early detection of knee health conditions. Vibroarthrography (VAG) is the most emerging tool to detect knee joint abnormalities. In this paper, an application of the empirical mode decomposition (EMD) is presented to discriminate between normal and abnormal knee joint VAG signals. EMD is employed to decompose VAG signals into several intrinsic mode functions (IMFs). Twelve different nonlinear, entropy, and shape-based features are elicited from each IMF provided by EMD. Kruskal–Wallis (K–W) test is employed to identify the best suitable features to discriminate between normal and knee joint affected VAG signals. The simulation results with the publicly available VAG database are included to show the effectiveness of the presented work.

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Singh, A., Gupta, K., Bajaj, V. (2023). Discrimination of Normal and Abnormal Knee Joint VAG Signals Using EMD. In: Rawat, S., Kumar, S., Kumar, P., Anguera, J. (eds) Proceedings of Second International Conference on Computational Electronics for Wireless Communications. Lecture Notes in Networks and Systems, vol 554. Springer, Singapore.

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