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Classifying individuals with and without patellofemoral pain syndrome using ground force profiles - Development of a method using functional data boosting.

Liew, Bernard XW and Rugamer, David and Abichandani, Deepa and De Nunzio, Alessandro Marco (2020) 'Classifying individuals with and without patellofemoral pain syndrome using ground force profiles - Development of a method using functional data boosting.' Gait and Posture, 80. pp. 90-95. ISSN 0966-6362

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Abstract

BACKGROUND:Predictors of recovery in patellofemoral pain syndrome (PFPS) currently used in prognostic models are scalar in nature, despite many physiological measures originally lying on the functional scale. Traditional modelling techniques cannot harness the potential predictive value of functional physiological variables. RESEARCH QUESTION:What is the classification performance of PFPS status of a statistical model when using functional ground reaction force (GRF) time-series? METHODS:Thirty-one individuals (control = 17, PFPS = 14) performed maximal countermovement jumps, on two force plates. The three-dimensional components of the GRF profiles were time-normalized between the start of the eccentric phase and take-off, and used as functional predictors. A statistical model was developed using functional data boosting (FDboost), for binary classification of PFPS statuses (control vs PFPS). The area under the Receiver Operating Characteristic curve (AUC) was used to quantify the model's ability to discriminate the two groups. RESULTS:The three predictors of GRF waveform achieved an average out-of-bag AUC of 93.7 %. A 1 % increase in applied medial force reduced the log odds of being in the PFPS group by 0.68 at 87 % of jump cycle. In the AP direction, a 1 % reduction in applied posterior force increased the log odds of being classified as PFPS by 1.10 at 70 % jump cycle. For the vertical GRF, a 1 % increase in applied force reduced the log odds of being classified in the PFPS group by 0.12 at 44 % of the jump cycle. SIGNIFICANCE:Using simple functional GRF variables collected during functionally relevant task, in conjunction with FDboost, produced clinically interpretable models that retain excellent classification performance in individuals with PFPS. FDboost may be an invaluable tool to be used in longitudinal cohort prognostic studies, especially when scalar and functional predictors are collected.

Item Type: Article
Uncontrolled Keywords: Patellofemoral pain syndrome; Jumping; Biomechanics; Machine learning; Functional regression
Divisions: Faculty of Science and Health
Faculty of Science and Health > Sport, Rehabilitation and Exercise Sciences, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 04 Dec 2020 17:33
Last Modified: 06 Jan 2022 14:15
URI: http://repository.essex.ac.uk/id/eprint/28947

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