Ali Selk Ghafari - Assistant Professor, Graduated, School of Science and Engineering, Sharif University of Technology International Campus, Kish Island
Parisa Nezafat - M.Sc. Graduated, School of Science and Engineering, Sharif University of Technology International Campus, Kish Island

The present study demonstrates an innovative approach to find the location of the sensors in designing a smart insole for assistive applications, leveraging experimental data-driven optimization techniques. The proposed insole is intended to provide real-time feedback and support for individuals with mobility impairments, with the goal of improving their gait dynamics and overall quality of life. By integrating advanced sensing technologies and machine learning algorithms, this smart insole aims to enhance the efficacy of rehabilitative therapies and improve patient outcomes. The experimental data-driven optimization methodology employed in this study enables the design of a customized insole that can be tailored to individual users, maximizing its effectiveness, and personalizing the rehabilitation process. In the proposed system eleven sensors are considered to cover different foot shapes and types. The findings of this investigation provide compelling evidence of the feasibility of this innovative methodology in enhancing the design and performance of smart insoles, as well as a notable correspondence between the optimal sensor placements and biomechanical landmarks.

Optimization, Smart Insole, Rehabilitation, Design