Smart Insole-Based Plantar Pressure Analysis for Healthy and Diabetic Feet Classification: Statistical vs. Machine Learning Approaches
Diabetes is a significant global health issue impacting millions. Approximately 26 million diabetics experience foot ulcers, with 20% ending up with amputations, resulting in high morbidity, mortality, and costs. Plantar pressure screening shows potential for early detection of Diabetic Foot Ulcers...
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MDPI AG
2024-11-01
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| author | Dipak Kumar Agrawal Watcharin Jongpinit Soodkhet Pojprapai Wipawee Usaha Pattra Wattanapan Pornthep Tangkanjanavelukul Timporn Vitoonpong |
| author_facet | Dipak Kumar Agrawal Watcharin Jongpinit Soodkhet Pojprapai Wipawee Usaha Pattra Wattanapan Pornthep Tangkanjanavelukul Timporn Vitoonpong |
| author_sort | Dipak Kumar Agrawal |
| collection | DOAJ |
| description | Diabetes is a significant global health issue impacting millions. Approximately 26 million diabetics experience foot ulcers, with 20% ending up with amputations, resulting in high morbidity, mortality, and costs. Plantar pressure screening shows potential for 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 from the insoles during walking, revealing that diabetic feet have higher plantar pressure than healthy feet, which is 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. |
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| issn | 2227-7080 |
| language | English |
| publishDate | 2024-11-01 |
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| spelling | doaj-art-6529323a6c4e4bf8bbd92240b28b842b2025-08-20T02:04:41ZengMDPI AGTechnologies2227-70802024-11-01121123110.3390/technologies12110231Smart Insole-Based Plantar Pressure Analysis for Healthy and Diabetic Feet Classification: Statistical vs. Machine Learning ApproachesDipak Kumar Agrawal0Watcharin Jongpinit1Soodkhet Pojprapai2Wipawee Usaha3Pattra Wattanapan4Pornthep Tangkanjanavelukul5Timporn Vitoonpong6School of Telecommunication Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, ThailandSchool of Ceramic Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, ThailandSchool of Ceramic Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, ThailandSchool of Telecommunication Engineering, Suranaree University of Technology, Nakhon Ratchasima 30000, ThailandDepartment of Rehabilitation Medicine, Khon Kaen University, Khon Kaen 40002, ThailandInstitute of Medicine, Suranaree University of Technology, Nakhon Ratchasima 30000, ThailandRehabilitation Department, Faculty of Medicine, Chulalongkorn University, Bangkok 10330, ThailandDiabetes is a significant global health issue impacting millions. Approximately 26 million diabetics experience foot ulcers, with 20% ending up with amputations, resulting in high morbidity, mortality, and costs. Plantar pressure screening shows potential for 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 from the insoles during walking, revealing that diabetic feet have higher plantar pressure than healthy feet, which is 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.https://www.mdpi.com/2227-7080/12/11/231diabetic footmachine learning algorithmsplantar pressurepressure sensorsmart insolestatistical analysis |
| spellingShingle | Dipak Kumar Agrawal Watcharin Jongpinit Soodkhet Pojprapai Wipawee Usaha Pattra Wattanapan Pornthep Tangkanjanavelukul Timporn Vitoonpong Smart Insole-Based Plantar Pressure Analysis for Healthy and Diabetic Feet Classification: Statistical vs. Machine Learning Approaches Technologies diabetic foot machine learning algorithms plantar pressure pressure sensor smart insole statistical analysis |
| title | Smart Insole-Based Plantar Pressure Analysis for Healthy and Diabetic Feet Classification: Statistical vs. Machine Learning Approaches |
| title_full | Smart Insole-Based Plantar Pressure Analysis for Healthy and Diabetic Feet Classification: Statistical vs. Machine Learning Approaches |
| title_fullStr | Smart Insole-Based Plantar Pressure Analysis for Healthy and Diabetic Feet Classification: Statistical vs. Machine Learning Approaches |
| title_full_unstemmed | Smart Insole-Based Plantar Pressure Analysis for Healthy and Diabetic Feet Classification: Statistical vs. Machine Learning Approaches |
| title_short | Smart Insole-Based Plantar Pressure Analysis for Healthy and Diabetic Feet Classification: Statistical vs. Machine Learning Approaches |
| title_sort | smart insole based plantar pressure analysis for healthy and diabetic feet classification statistical vs machine learning approaches |
| topic | diabetic foot machine learning algorithms plantar pressure pressure sensor smart insole statistical analysis |
| url | https://www.mdpi.com/2227-7080/12/11/231 |
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