PreprintArticleVersion 1This version is not peer-reviewed
Smart Insole-Based Plantar Pressure Analysis for Healthy and Diabetic Feet Classification: A Comparative Study of Statistic-Based and Machine Learning-Based Approaches
Version 1
: Received: 17 October 2024 / Approved: 17 October 2024 / Online: 17 October 2024 (12:56:14 CEST)
How to cite:
Agrawal, D. K.; Jongpinit, W.; Pojprapai, S.; Usaha, W.; Wattanapan, P.; Tangkanjanavelukul, P.; Vitoonpong, T. Smart Insole-Based Plantar Pressure Analysis for Healthy and Diabetic Feet Classification: A Comparative Study of Statistic-Based and Machine Learning-Based Approaches. Preprints2024, 2024101389. https://doi.org/10.20944/preprints202410.1389.v1
Agrawal, D. K.; Jongpinit, W.; Pojprapai, S.; Usaha, W.; Wattanapan, P.; Tangkanjanavelukul, P.; Vitoonpong, T. Smart Insole-Based Plantar Pressure Analysis for Healthy and Diabetic Feet Classification: A Comparative Study of Statistic-Based and Machine Learning-Based Approaches. Preprints 2024, 2024101389. https://doi.org/10.20944/preprints202410.1389.v1
Agrawal, D. K.; Jongpinit, W.; Pojprapai, S.; Usaha, W.; Wattanapan, P.; Tangkanjanavelukul, P.; Vitoonpong, T. Smart Insole-Based Plantar Pressure Analysis for Healthy and Diabetic Feet Classification: A Comparative Study of Statistic-Based and Machine Learning-Based Approaches. Preprints2024, 2024101389. https://doi.org/10.20944/preprints202410.1389.v1
APA Style
Agrawal, D. K., Jongpinit, W., Pojprapai, S., Usaha, W., Wattanapan, P., Tangkanjanavelukul, P., & Vitoonpong, T. (2024). Smart Insole-Based Plantar Pressure Analysis for Healthy and Diabetic Feet Classification: A Comparative Study of Statistic-Based and Machine Learning-Based Approaches. Preprints. https://doi.org/10.20944/preprints202410.1389.v1
Chicago/Turabian Style
Agrawal, D. K., Pornthep Tangkanjanavelukul and Timporn Vitoonpong. 2024 "Smart Insole-Based Plantar Pressure Analysis for Healthy and Diabetic Feet Classification: A Comparative Study of Statistic-Based and Machine Learning-Based Approaches" Preprints. https://doi.org/10.20944/preprints202410.1389.v1
Abstract
Diabetes is a significant global health issue impacting millions. Approximately 26 million diabetics experience foot ulcers, with 20% leading to amputations, resulting in high morbidity, mortality, and costs. Plantar pressure screening shows potential for the early detection of Diabetic Foot Ulcers (DFUs). Although foot ulcers often occur due to excessive pressure on the soles during dynamic activities, most studies focus on static pressure measurements. This study’s primary objective is to apply wireless plantar pressure sensor-embedded insoles to classify and detect diabetic feet from healthy ones based on dynamic plantar pressure. The secondary objective is to compare statistical-based and Machine Learning (ML) classification methods. Data from 150 subjects were collected using the insoles during walking, revealing that diabetic feet have higher plantar pressure than healthy feet, consistent with prior research. The Adaptive Boosting (AdaBoost) ML model achieved the highest accuracy of 0.85, outperforming the statistical method, which had an accuracy of 0.67. These findings suggest that ML models, combined with pressure sensor-embedded insoles, can effectively classify healthy and diabetic feet using plantar pressure features. Future research will focus on using these insoles with ML to classify various stages of diabetic neuropathy, aiming for early prediction of foot ulcers in home settings.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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