  Artificial intelligence (AI) has made significant advances in recent years
and opened up new possibilities in exploring applications in various fields
such as biomedical, robotics, education, industry, etc. Among these fields,
human hand gesture recognition is a subject of study that has recently emerged
as a research interest in robotic hand control using electromyography (EMG).
Surface electromyography (sEMG) is a primary technique used in EMG, which is
popular due to its non-invasive nature and is used to capture gesture movements
using signal acquisition devices placed on the surface of the forearm.
Moreover, these signals are pre-processed to extract significant handcrafted
features through time and frequency domain analysis. These are helpful and act
as input to machine learning (ML) models to identify hand gestures. However,
handling multiple classes and biases are major limitations that can affect the
performance of an ML model. Therefore, to address this issue, a new mixture of
experts extra tree (MEET) model is proposed to identify more accurate and
effective hand gesture movements. This model combines individual ML models
referred to as experts, each focusing on a minimal class of two. Moreover, a
fully trained model known as the gate is employed to weigh the output of
individual expert models. This amalgamation of the expert models with the gate
model is known as a mixture of experts extra tree (MEET) model. In this study,
four subjects with six hand gesture movements have been considered and their
identification is evaluated among eleven models, including the MEET classifier.
Results elucidate that the MEET classifier performed best among other
algorithms and identified hand gesture movement accurately.
