MEET: Mixture of Experts Extra Tree-Based sEMG Hand Gesture Identification
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Electromyography (EMG)Gesture RecognitionHand Gesture RecognitionHand-Gesture RecognitionMixture-of-ExpertsSimilar Papers 제목 키워드 기반
MoEMba: A Mamba-based Mixture of Experts for High-Density EMG-based Hand Gesture Recognition
High-Density surface Electromyography (HDsEMG) has emerged as a pivotal resource for Human-Computer Interaction (HCI), offering direct insights into muscle activities and motion intentions. However, a significant challen…
Gesture RecognitionHand Gesture RecognitionHand-Gesture RecognitionMamba+1Tree-gated Deep Mixture-of-Experts For Pose-robust Face Alignment
Face alignment consists of aligning a shape model on a face image. It is an active domain in computer vision as it is a preprocessing for a number of face analysis and synthesis applications. Current state-of-the-art met…
Face AlignmentMixture-of-ExpertsregressionRobust Face AlignmentA Laplacian Gaussian Mixture Model for Surface EMG Signals of Human Arm Activity
The probability density function (pdf) of surface Electromyography (sEMG) signals follows any one of the standalone standard distributions: the Gaussian or the Laplacian. Further, the choice of the model is dependent on …
Biased Mixtures Of Experts: Enabling Computer Vision Inference Under Data Transfer Limitations
We propose a novel mixture-of-experts class to optimize computer vision models in accordance with data transfer limitations at test time. Our approach postulates that the minimum acceptable amount of data allowing for hi…
Action ClassificationImage Super-ResolutionMixture-of-ExpertsSuper-ResolutionYOLO Meets Mixture-of-Experts: Adaptive Expert Routing for Robust Object Detection
This paper presents a novel Mixture-of-Experts framework for object detection, incorporating adaptive routing among multiple YOLOv9-T experts to enable dynamic feature specialization and achieve higher mean Average Preci…
Robust Object Detection