<p>Data.</p>

These standardized weight distribution patterns are used to identify deviations associated with musculoskeletal conditions that support early personalized rehabilitation and training strategies. The proposed model is implemented on a real-time embedded system and cloud platform. The embedded system uses a K230 RISC-V processor and performs real-time data acquisition, on-board data processing, and visualization of foot weight distribution.

The application running on cloud platform performs statistical analysis and applies a classical support vector machine (SVM) for classifying abnormal pressure patterns. A Convolution Neural Network (CNN) based model is developed that learns fit and un-fit foot patterns from the 3232 pressure maps. The model results show improved detection of dysfunctional weight distribution.

The CNN approach shows that AI model can perform advanced predictive analytics in gait and posture assessment. The analysis is limited to static standing posture and does not include dynamic gait assessment. The normative ranges are derived primarily from subjects up to approximately 80–90 kg, as no musculoskeletally fit participants were identified in the heavier weight groups (90–120 kg); therefore, generalization to heavier individuals requires caution and further data collection.</p></div>