Intelligent Footwear Material Recommendation System: Using Machine Learning For Refined Slip Resistance

Abstract - Slip and fall incidents create major safety hazards in different settings because of the inappropriate choice of footwear. Conventional slip resistance standards do not consider user-specific factors and dynamic conditions. This article introduces an Intelligent Slip Resistance Recommender System, a machine learning system utilizing a Random Forest Classifier to suggest best footwear materials according to user input (age, weight, shoe size, activity type) and environmental conditions (surface type and conditions).The system consumes inputs through a multi-stage pipeline, encoding categorical variables and predicting material suitability with 92% accuracy, outperforming traditional methods. It offers context-aware recommendations, such as Vibram for outdoor rough surfaces and non-marking rubber for indoor polished floors. The web interface is easy to use, making it available to a wide audience of professionals and differently-abled users.This system connects material science, human factors, and AI to enhance workplace safety, mitigate fall hazards among the elderly, and optimize decision-making in footwear choice. Future development involves the extension of datasets and the inclusion of real-time environmental data for dynamic suggestions.