A systematic review of artificial neural network techniques for analysis of foot plantar pressure

• Autorzy: Wang Chongguang , Evans Kerrie , Hartley Dean , Morrison Scott , Veidt Martin , Wang Gui

Identyfikatory

DOI

10.1016/j.bbe.2024.01.005

• Języki publikacji: EN

Abstrakty

EN

Plantar pressure distribution offers insights into foot function, gait mechanics, and foot-related issues. This systematic review presents an analysis of the use of artificial neural network techniques in the context of plantar pressure analysis. 60 studies were included in the review. Sample size, pathology, pressure sensor number, data collection device, utilization of other sensor devices, ground-truth methods, pre-processing dataset, neural network type, and evaluation metrics were evaluated. Utilization of customized wearable footwear devices for the acquisition of data was common amongst both healthy participants and patients. Inertial measurement units emerged as an effective compensatory measure to address the limitations associated with the distribution of plantar pressure. Ground truth methods predominantly relied on the usage of both annotations and reference devices. Multilayer perceptron, convolutional neural networks, and recurrent neural networks were identified as the most frequently employed artificial neural network algorithms across the reviewed studies. Finally, the evaluation of performance largely drew upon statistical descriptions and other machine learning methods. This review provides a comprehensive understanding of the use of artificial neural network techniques in plantar pressure analysis, highlighting opportunities for future research.

Słowa kluczowe

EN

artificial neural network; feature extraction; gait analysis; machine learning; plantar pressure; wearable sensor

PL

sztuczna sieć neuronowa; ekstrakcja cech; analiza chodu; uczenie maszynowe; ciśnienie podeszwowe

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2024
• Tom: Vol. 44, no. 1
• Strony: 197--208
• Opis fizyczny: Bibliogr. 110 poz., rys., tab., wykr.

Twórcy

autor

Wang Chongguang

• School of Mechanical and Mining Engineering, The University of Queensland, St Lucia, QLD 4072, Australia

autor

Evans Kerrie

• School of Mechanical and Mining Engineering, The University of Queensland, St Lucia, QLD 4072, Australia
• Healthia Limited, Bowen Hills, QLD 4006, Australia
• Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW 2050, Australia

autor

Hartley Dean

• School of Mechanical and Mining Engineering, The University of Queensland, St Lucia, QLD 4072, Australia
• Healthia Limited, Bowen Hills, QLD 4006, Australia
• iOrthotics Pty Ltd, Windsor, QLD 4030, Australia

autor

Morrison Scott

• School of Mechanical and Mining Engineering, The University of Queensland, St Lucia, QLD 4072, Australia
• iOrthotics Pty Ltd, Windsor, QLD 4030, Australia

autor

Veidt Martin

• School of Mechanical and Mining Engineering, The University of Queensland, St Lucia, QLD 4072, Australia

autor

Wang Gui

• School of Mechanical and Mining Engineering, The University of Queensland, St Lucia, QLD 4072, Australia

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).