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2026
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| Online Access: | https://doi.org/10.5281/zenodo.18203349 |
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| author | Acuña Acuña, Edwin Gerardo |
| author_facet | Acuña Acuña, Edwin Gerardo |
| contents | <p>This dataset presents a comprehensive, high-quality synthetic database designed to support research on <strong>sensor-based smart insole systems for continuous plantar pressure monitoring and foot health assessment</strong>. The dataset has been developed to facilitate methodological validation, algorithm benchmarking, and simulation-based analysis in studies related to wearable health technologies, biomechanics, and intelligent medical monitoring systems.</p> <p>The database comprises <strong>500 anonymized patient records</strong>, each associated with structured plantar pressure measurements collected through a virtual smart insole platform. For every patient, the dataset includes demographic and anthropometric variables, automatically derived indicators such as body mass index (BMI), and risk stratification attributes relevant to foot health analysis. No personally identifiable information is included, ensuring compliance with ethical research standards and data protection principles.</p> <p>Plantar pressure data are organized at the <strong>session, foot (left and right), and plantar region level</strong>, enabling detailed spatial and bilateral analysis. The measurements cover key biomechanical indicators commonly used in clinical and engineering studies, including peak pressure, mean pressure, pressure-time integral, contact time, and asymmetry indices. This structure supports advanced statistical evaluation, gait analysis, and machine learning workflows.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18203349 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Smart Insole Plantar Pressure Dataset for Continuous Foot Health Monitoring and Lesion-Oriented Simulation Acuña Acuña, Edwin Gerardo <p>This dataset presents a comprehensive, high-quality synthetic database designed to support research on <strong>sensor-based smart insole systems for continuous plantar pressure monitoring and foot health assessment</strong>. The dataset has been developed to facilitate methodological validation, algorithm benchmarking, and simulation-based analysis in studies related to wearable health technologies, biomechanics, and intelligent medical monitoring systems.</p> <p>The database comprises <strong>500 anonymized patient records</strong>, each associated with structured plantar pressure measurements collected through a virtual smart insole platform. For every patient, the dataset includes demographic and anthropometric variables, automatically derived indicators such as body mass index (BMI), and risk stratification attributes relevant to foot health analysis. No personally identifiable information is included, ensuring compliance with ethical research standards and data protection principles.</p> <p>Plantar pressure data are organized at the <strong>session, foot (left and right), and plantar region level</strong>, enabling detailed spatial and bilateral analysis. The measurements cover key biomechanical indicators commonly used in clinical and engineering studies, including peak pressure, mean pressure, pressure-time integral, contact time, and asymmetry indices. This structure supports advanced statistical evaluation, gait analysis, and machine learning workflows.</p> |
| title | Smart Insole Plantar Pressure Dataset for Continuous Foot Health Monitoring and Lesion-Oriented Simulation |
| url | https://doi.org/10.5281/zenodo.18203349 |