Abstract In recent years, Hand Gesture Recognition (HGR) devices have been designed to recognize gestures in real time using machine-learning classifiers (MLCs). However, the performance of these classifiers heavily relies on the tuning of their hyperparameters on real-time data. In this regard, this study provides a Linear Population Size Reduction Success-History Adaptation Differential Evolution (L-SHADE)-based optimized Extra Tree (ET) MLC framework for HGR. The study includes real-time sEMG signals from two forearm muscles to capture six distinct hand gesture movements. To recognize the gesture, this work employed ten MLCs. Among these ET classifier demonstrates the highest accuracy without optimizing the hyperparameters. To further enhance performance, ten optimization algorithms, along with the ET classifier, are considered, where the L-SHADE optimized ET framework outperforms the others. To validate the proposed framework, a consistent system environment has been used for both acquired and public datasets. On the acquired data, the mean accuracy improves from 84.14% to 87.89% using ET with the L-SHADE optimization framework while the mean computational time is reduced from 8.62 to 3.16 milliseconds. Similarly, the publicly available 15-hand gesture classification dataset demonstrated a mean accuracy improvement of more than 3.0%.
Gehlot et al. (Tue,) studied this question.