This study aims to evaluate the validity and reliability of a proposed plantar pressure assessment intrument based on an implementation of machine learning into optical pedography.

This study aims to evaluate the validity and reliability of a proposed plantar pressure assessment intrument based on an implementation of machine learning into optical pedography. The whole process will be divided into distinct steps required for the targeted outcome, which includes:

1. collecting visual data (podoscope foot pictures) and training a segmentation machine learning-based algorithm designed for recognizing only feet area, a total of atleast 30 participants performing 9 different standing positions - over 270 usable pictures for training and functionality validation
2. collecting personal, visual and pressure data (participant weight, podoscope foot pictures, pedobarographic platform measurements) and training a machine/deep learning-based model designed for feet pressure distribution areas identification and quantification, a total of estimated 60 participants undergoing 5 alternating, as similar as possible, measurements on podoscope and pedobarographic platform
3. evaluating the validity and reliability of a new plantar pressure measuring instrument following the same imaging procedure as described in step 2, a total of estimated 60 participants

Inclusion Criteria:

• A healthy individual with no absence of lower extremity
• Aged ≥18 years
• Willingness to participate and ability to follow the assessors instructions.

Exclusion Criteria:

• Presence of foot diseases alternating foot contact area
• Cognitive or psychiatric disorders limiting cooperation
• Lack of informed consent or non-compliance during imaging

Healthy individuals willing to participate in the study protocol

Individual participant data will not be shared due to patient privacy and institutional data protection policies