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ORIGINAL RESEARCH article

Front. Bioeng. Biotechnol.
Sec. Biomechanics
Volume 12 - 2024 | doi: 10.3389/fbioe.2024.1417497

Predicting Improvement in Biofeedback Gait Training Using Short-Term Spectral Features from Minimum Foot Clearance Data

Provisionally accepted
Nandini Sengupta Nandini Sengupta 1*Rezaul Begg Rezaul Begg 2Aravinda Rao Aravinda Rao 1Soheil Bajelan Soheil Bajelan 2Catherine M. Said Catherine M. Said 3,4,5,6Marimuthu Palaniswami Marimuthu Palaniswami 1
  • 1 The University of Melbourne, Parkville, Australia
  • 2 Institute of Health and Sport, College of Health and Biomedicine, Victoria University, Melbourne, Victoria, Australia
  • 3 Department of Physiotherapy, Melbourne School of Health Sciences, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Victoria, Australia
  • 4 Department of Physiotherapy, Western Health, St Albans, Australia
  • 5 Australian Institute for Musculoskeletal Science (AIMSS), Melbourne, Victoria, Australia
  • 6 Physiotherapy Department, Austin Health, Heidelberg, Australia

The final, formatted version of the article will be published soon.

    Stroke rehabilitation interventions require multiple training sessions and repeated assessments to evaluate the improvements from training. Biofeedback-based treadmill training often involves 10 or more sessions to determine its effectiveness. The training and assessment process incurs time, labor, and cost to determine whether the training produces positive outcomes. Predicting the effectiveness of gait training based on baseline minimum foot clearance (MFC) data would be highly beneficial, potentially saving resources, costs, and patient time. This work proposes novel features using the Short-term Fourier Transform (STFT)-based magnitude spectrum of MFC data to predict the effectiveness of biofeedback training. This approach enables tracking nonstationary dynamics and capturing stride-to-stride MFC value fluctuations, providing a compact representation for efficient processing compared to time-domain analysis alone. The proposed STFT-based features outperform existing wavelet, histogram, and Poincar é-based features with a maximum accuracy of 95%, F1 score of 96%, sensitivity of 93.33% and specificity of 100%.The proposed features are also statistically significant (p<0.001) compared to the descriptive statistical features extracted from the MFC series and the tone and entropy features extracted from the MFC percentage index series. The study found that short-term spectral components and the windowed mean value (DC value) possess predictive capabilities regarding the success of biofeedback training. The higher spectral amplitude and lower variance in the lower frequency zone indicate lower chances of improvement, while the lower spectral amplitude and higher variance indicate higher chances of improvement.

    Keywords: stroke rehabilitation, biofeedback, Treadmill training, Interventions, machine learning, Signal processing

    Received: 15 Apr 2024; Accepted: 13 Aug 2024.

    Copyright: © 2024 Sengupta, Begg, Rao, Bajelan, Said and Palaniswami. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Nandini Sengupta, The University of Melbourne, Parkville, Australia

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.