A Multi-label Classification Approach to Increase Expressivity of EMG-based Gesture Recognition

• URL: http://arxiv.org/abs/2309.12217v1
• Date: Wed, 13 Sep 2023 20:21:41 GMT
• Title: A Multi-label Classification Approach to Increase Expressivity of EMG-based Gesture Recognition
• Authors: Niklas Smedemark-Margulies, Yunus Bicer, Elifnur Sunger, Stephanie Naufel, Tales Imbiriba, Eugene Tunik, Deniz Erdo\u{g}mu\c{s}, Mathew Yarossi
• Abstract summary: The aim of this study is to efficiently increase the expressivity of surface electromyography-based (sEMG) gesture recognition systems. We use a problem transformation approach, in which actions were subset into two biomechanically independent components.
• Score: 4.701158597171363
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Objective: The objective of the study is to efficiently increase the expressivity of surface electromyography-based (sEMG) gesture recognition systems. Approach: We use a problem transformation approach, in which actions were subset into two biomechanically independent components - a set of wrist directions and a set of finger modifiers. To maintain fast calibration time, we train models for each component using only individual gestures, and extrapolate to the full product space of combination gestures by generating synthetic data. We collected a supervised dataset with high-confidence ground truth labels in which subjects performed combination gestures while holding a joystick, and conducted experiments to analyze the impact of model architectures, classifier algorithms, and synthetic data generation strategies on the performance of the proposed approach. Main Results: We found that a problem transformation approach using a parallel model architecture in combination with a non-linear classifier, along with restricted synthetic data generation, shows promise in increasing the expressivity of sEMG-based gestures with a short calibration time. Significance: sEMG-based gesture recognition has applications in human-computer interaction, virtual reality, and the control of robotic and prosthetic devices. Existing approaches require exhaustive model calibration. The proposed approach increases expressivity without requiring users to demonstrate all combination gesture classes. Our results may be extended to larger gesture vocabularies and more complicated model architectures.

Related papers

• Decomposed Vector-Quantized Variational Autoencoder for Human Grasp Generation [27.206656215734295]
  We propose a novel Decomposed Vector-Quantized Variational Autoencoder (DVQ-VAE) to generate realistic human grasps. Part-aware decomposed architecture facilitates more precise management of the interaction between each component of hand and object. Our model achieved about 14.1% relative improvement in the quality index compared to the state-of-the-art methods in four widely-adopted benchmarks.
  arXiv  Detail & Related papers  (2024-07-19T06:41:16Z)
• Benchmark on Drug Target Interaction Modeling from a Structure Perspective [48.60648369785105]
  Drug-target interaction prediction is crucial to drug discovery and design. Recent methods, such as those based on graph neural networks (GNNs) and Transformers, demonstrate exceptional performance across various datasets. We conduct a comprehensive survey and benchmark for drug-target interaction modeling from a structure perspective, via integrating tens of explicit (i.e., GNN-based) and implicit (i.e., Transformer-based) structure learning algorithms.
  arXiv  Detail & Related papers  (2024-07-04T16:56:59Z)
• Learning Manipulation by Predicting Interaction [85.57297574510507]
  We propose a general pre-training pipeline that learns Manipulation by Predicting the Interaction. The experimental results demonstrate that MPI exhibits remarkable improvement by 10% to 64% compared with previous state-of-the-art in real-world robot platforms.
  arXiv  Detail & Related papers  (2024-06-01T13:28:31Z)
• Learning Mutual Excitation for Hand-to-Hand and Human-to-Human Interaction Recognition [22.538114033191313]
  We propose a mutual excitation graph convolutional network (me-GCN) by stacking mutual excitation graph convolution layers. Me-GC learns mutual information in each layer and each stage of graph convolution operations. Our proposed me-GC outperforms state-of-the-art GCN-based and Transformer-based methods.
  arXiv  Detail & Related papers  (2024-02-04T10:00:00Z)
• Towards a Unified Transformer-based Framework for Scene Graph Generation and Human-object Interaction Detection [116.21529970404653]
  We introduce SG2HOI+, a unified one-step model based on the Transformer architecture. Our approach employs two interactive hierarchical Transformers to seamlessly unify the tasks of SGG and HOI detection. Our approach achieves competitive performance when compared to state-of-the-art HOI methods.
  arXiv  Detail & Related papers  (2023-11-03T07:25:57Z)
• Fast and Expressive Gesture Recognition using a Combination-Homomorphic Electromyogram Encoder [21.25126610043744]
  We study the task of gesture recognition from electromyography (EMG) We define combination gestures consisting of a direction component and a modifier component. New subjects only demonstrate the single component gestures. We extrapolate to unseen combination gestures by combining the feature vectors of real single gestures to produce synthetic training data.
  arXiv  Detail & Related papers  (2023-10-30T20:03:34Z)
• A Comparative Study of Data Augmentation Techniques for Deep Learning Based Emotion Recognition [11.928873764689458]
  We conduct a comprehensive evaluation of popular deep learning approaches for emotion recognition. We show that long-range dependencies in the speech signal are critical for emotion recognition. Speed/rate augmentation offers the most robust performance gain across models.
  arXiv  Detail & Related papers  (2022-11-09T17:27:03Z)
• Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation [56.264157127549446]
  Speech emotion recognition (SER) is a challenging task that plays a crucial role in natural human-computer interaction. One of the main challenges in SER is data scarcity. We propose a transfer learning strategy combined with spectrogram augmentation.
  arXiv  Detail & Related papers  (2021-08-05T10:39:39Z)
• Domain Adaptive Robotic Gesture Recognition with Unsupervised Kinematic-Visual Data Alignment [60.31418655784291]
  We propose a novel unsupervised domain adaptation framework which can simultaneously transfer multi-modality knowledge, i.e., both kinematic and visual data, from simulator to real robot. It remedies the domain gap with enhanced transferable features by using temporal cues in videos, and inherent correlations in multi-modal towards recognizing gesture. Results show that our approach recovers the performance with great improvement gains, up to 12.91% in ACC and 20.16% in F1score without using any annotations in real robot.
  arXiv  Detail & Related papers  (2021-03-06T09:10:03Z)
• Relational Graph Learning on Visual and Kinematics Embeddings for Accurate Gesture Recognition in Robotic Surgery [84.73764603474413]
  We propose a novel online approach of multi-modal graph network (i.e., MRG-Net) to dynamically integrate visual and kinematics information. The effectiveness of our method is demonstrated with state-of-the-art results on the public JIGSAWS dataset.
  arXiv  Detail & Related papers  (2020-11-03T11:00:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.