Fuzzy-rough based decision system for gait adopting instance selection
A fuzzy rough set theory based gait decision system is presented for diagnosis of fall risk. Distracted walking is investigated. Gait cycles of thirty young participants were monitored while they walked across a walkway at a self-selected pace and also while being distracted by performing a task alongside walking. Results shows distracted walking being similar to impaired walking of an elderly person. The aim is to adopt instance selection methods to eliminate redundant or noisy sample from training data thus preventing the system from detecting erroneous gait patterns or deviations. Decision systems have great potential in medical informatics to serve as diagnostic tools. Results show that instance selection improves the efficacy of various classifiers in detecting distracted gait. The model is capable of assessing gait based on the easily obtainable features and categorizing them in terms of fall risk. This will help to identify individuals at risk in fall prevention management.