Development of an Expert System for Advanced Podiatric Treatments and Ergonomics

An expert system tailored for foot pressure analysis and imprint detection, with a focus on enhancing podiatric treatments and ergonomics. The system comprises three key components: a foot pressure analyser, an image processing-based foot imprint analyser, and a machine learning-driven data analyser. The foot pressure analyser employs an array of FSR sensors with piezo resistive material Velostat, accommodating various foot types. The collected data is processed using thermal image algorithms to create foot pressure images. The image processing unit of the system utilizes edge detection techniques to extract relevant features from the foot pressure images, forming a dataset for machine learning. A neural network is trained using this dataset to identify lower limb abnormalities accurately. The expert system encompasses an extensive knowledge base, storing foot anatomy, pressure point norms, common foot conditions, footwear information, and also analyses lower limb extremities like arthritis and synovial accumulations etc., especially helps in geriatric care. The system generates comprehensive reports summarizing the analysis results, facilitating treatment planning for specific applications, age groups, genders, and foot shapes and giving real-time feedback and recommendations based on the Expert’s knowledge to aid in treatment planning, ensuring accessibility and data security. Future developments include 3D printing integration, research support, and machine learning enhancements. The system promises to advance foot health diagnostics and treatments significantly, benefiting healthcare professionals and patients.