Abstract
Patellofemoral pain syndrome (PFPS) is common in people who participate in sports and can greatly affect their daily activities. Thus, it is important to find the related factors and diagnose it properly. Most existing computer-assist methods for PFPS diagnosis involve complex biomechanical models and parameters, which prevent their clinical usage. To address this issue, this paper proposes a new method to diagnose PFPS by using the extreme learning machine (ELM). The proposed method requires only a few inputs including joint angles and surface EMG signals; but yields a higher accuracy (82.7%) than the state-of-the-art methods, i.e. K-Nearest Neighbor (62.6%), Random Decision Forests (67.3%), Support Vector Machine (63.3%), Naïve Bayes (58.6%) and the Multilayer Perceptron (75.4%). As such, the proposed method allows for the diagnosis of PFPS in daily environment, eliminating the need for expensive and special clinical instruments.
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References
Taunton, E.J.: A retrospective case-control analysis of 2002 running injuries. Br. J. Sports Med. 36(2), 95–101 (2002)
Ferrari, D., Kuriki, H.U., Silva, C.R., et al.: Diagnostic accuracy of the electromyography parameters associated with anterior knee pain in the diagnosis of patellofemoral pain syndrome. Arch. Phys. Med. Rehabil. 95(8), 1521–1526 (2014)
Nunes, G.S., Stapait, E.L., Kirsten, M.H., et al.: Clinical test for diagnosis of patellofemoral pain syndrome: Systematic review with meta-analysis. Phys. Ther. Sport 14(1), 54–59 (2013)
Cook, C., Mabry, L., Reiman, M.P., et al.: Best tests/clinical findings for screening and diagnosis of patellofemoral pain syndrome: a systematic review. Physiotherapy 98(2), 93–100 (2012)
Lankhorst, N.E., Bierma-Zeinstra, S.M.A., Van Middelkoop, M.: Factors associated with patellofemoral pain syndrome: a systematic review. Br. J. Sports Med. 47(4), 193–206 (2013)
Myer, G.D., Ford, K.R., Foss, K.D.B., et al.: The incidence and potential pathomechanics of patellofemoral pain in female athletes. Clin. Biomech. 25(7), 700–707 (2010)
Boling, M.C., Padua, D.A., Marshall, S.W., et al.: A prospective investigation of biomechanical risk factors for patellofemoral pain syndrome: the joint undertaking to monitor and prevent ACL injury (JUMP-ACL) cohort. Am. J. Sports Med. 37(11), 2108–2116 (2009)
Myer, G.D., Ford, K.R., Foss, K.D.B., et al.: A predictive model to estimate knee-abduction moment: implications for development of a clinically applicable patellofemoral pain screening tool in female athletes. J. Athletic Train. 49(3), 389–398 (2014)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: Theory and applications. Neurocomputing 70(1–3), 489–501 (2006)
Besier, T.F., Fredericson, M., Gold, G.E., et al.: Knee muscle forces during walking and running in patellofemoral pain patients and pain-free controls. J. Biomech. 42(7), 898–905 (2009)
Fulkerson, J.P., Arendt, E.A.: Anterior knee pain in females. Clin. Orthop. Relat. Res. 372, 69–73 (2000)
Huang, G.B., Babri, H.A.: Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions. IEEE Trans. Neural Netw. 9(1), 224–229 (1998)
Huang, G.B.: Learning capability and storage capacity of two-hidden-layer feedforward networks. IEEE Trans. Neural Netw. 14(2), 274–281 (2003)
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Shi, W., Xiong, B., Huang, M., Du, M., Yang, Y. (2021). An Extreme Learning Machine Method for Diagnosis of Patellofemoral Pain Syndrome. In: Cao, J., Vong, C.M., Miche, Y., Lendasse, A. (eds) Proceedings of ELM2019. ELM 2019. Proceedings in Adaptation, Learning and Optimization, vol 14. Springer, Cham. https://doi.org/10.1007/978-3-030-58989-9_3
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DOI: https://doi.org/10.1007/978-3-030-58989-9_3
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