Surface Hardness Mapping With Flexible Insoles Using Machine Learning

There are several use-cases where the hardness of a surface can be an important parameter - construction, infrastructure, sports fields, and so on. At present, there are no low-cost, thin-film, and wearable-based solutions for this problem. This study presents the development and evaluation of an insole-based sensing system for detecting and characterizing variations in surface conditions. The system integrates velostat, a piezoresistive material, into an insole to accurately capture plantar pressure variations as users traverse diverse terrains. The pressure data are subsequently processed to generate a detailed map differentiating various surface types. Experimental validation demonstrates that the system is capable of successfully mapping two distinct surfaces with an accuracy of 88.8 %. Multiple machine learning algorithms were analyzed to optimize the system performance and validate its efficacy. This approach has significant implications for the maintenance of a sports field, the optimization of athlete performance, and the prevention of injury.