Human gait analysis is a well-defined technique for human identification and tracking at distance based on their walking style. It plays an important role in the video surveillance, medical, and defense applications. A number of sensors such as MEMS accelerators, gyroscopes, pressure, and position were deployed for measuring the different gait signals from the body and utilized for the different analysis of human behavior. To effectively reconcile these innovations in medical profession, system is required to identify the most important body features which have an impact on an accurate diagnosis and classification. This study proposes the novel method FOW which intended to choose the best gait features as optimization strategy based on the hybrid integration of whale and firefly algorithms. This approach is utilized for approximating the performance of different classification benchmarks in order to have an efficient medical diagnosis system. In fact, classification issue for the whole set of features is terminated, and it can be significantly pruned. Experimentation has been carried for 35 individuals in which 16 features has been recorded and analyzed. Moreover, the proposed methodology has tested with the different learning algorithms in which integrating with the extreme learning machine has produced nearly 98.5% of accuracy and also outperformed the other existing selection methodologies such as accuracy, sensitivity, and specificity on different classification platforms. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.