Anash, AdeliyaOralkhan, SabyrzhanIssabek, MoldirNuriya, Nurbergenova2024-07-152024-07-152024-05Anash, A., Oralkhan, S., Issabek, M., & Nurbergenova, N. (2027). The design of self-charging sensor induced simplified Insole-Based prototypes with pressure measurement for fast screening of Flat-Foot. Nazarbayev University School of Engineering and Digital Scienceshttp://nur.nu.edu.kz/handle/123456789/8112Flatfoot is an orthopedic foot malformation in which the inner arch of the foot virtually or completely flattens during static or dynamic motions. This abnormal deformation can negatively affect the musculoskeletal system, leading to chronic pains and other conditions that may severely deteriorate a person’s quality of life if not treated timely. Therefore, there is a need for continuous monitoring of food conditions, and currently, available screening methods may not be sustainable in terms of objectivity, time, and money. This research aims to design and fabricate an insole-based screening device that would offer accurate and accessible screening. In order to implement our objectives, the self-powered triboelectric nanogenerators (TENG) were used as tactile pressure sensors for the insole since they propose such advantages as uncomplicated fabrication and design operations, cost-effectiveness, extensive lifetime, and high output power. TENGs’ main purpose is converting mechanical energy into electrical energy. In other words, the energy generated from the movement of the object is translated into electric output and recorded by the Arduino circuits. The collected data is analyzed using machine learning algorithms for the system to be able to immediately recognize the flatfoot conditions after undergoing the training sessions. To collect data, 82 participants were asked to march in one place and walk the same amount of time and distance to get similar numbers of outputs from each operation. The analysis showed that the middle sensors of the insoles generated much higher electricity when they were attached to people with flatfoot conditions and that they exhibited relatively uniform equal pressure distribution throughout the foot. In contrast, people with normal feet put more pressure on the front and back side of the foot. The overall accuracy of the machine learning system reached 81%, indicating that the designed insole has a high potential to be used as a flatfoot detecting device commercially.enAttribution-NonCommercial-NoDerivs 3.0 United StatesResearch Subject Categories::TECHNOLOGYResearch Subject Categories::MEDICINEType of access: EmbargoMachine Learningtriboelectric nanogeneratorsmart sensing insoleinsolesensorflat-foot detectiongait analysisCapstone Project