An LSTM Feature Imitation Network for Hand Movement Recognition from sEMG Signals

• URL: http://arxiv.org/abs/2405.19356v1
• Date: Thu, 23 May 2024 21:45:15 GMT
• Title: An LSTM Feature Imitation Network for Hand Movement Recognition from sEMG Signals
• Authors: Chuheng Wu, S. Farokh Atashzar, Mohammad M. Ghassemi, Tuka Alhanai,
• Abstract summary: We propose utilizing a feature-imitating network (FIN) for closed-form temporal feature learning over a 300ms signal window on Ninapro DB2. We then explore transfer learning capabilities by applying the pre-trained LSTM-FIN for tuning to a downstream hand movement recognition task.
• Score: 2.632402517354116
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Surface Electromyography (sEMG) is a non-invasive signal that is used in the recognition of hand movement patterns, the diagnosis of diseases, and the robust control of prostheses. Despite the remarkable success of recent end-to-end Deep Learning approaches, they are still limited by the need for large amounts of labeled data. To alleviate the requirement for big data, researchers utilize Feature Engineering, which involves decomposing the sEMG signal into several spatial, temporal, and frequency features. In this paper, we propose utilizing a feature-imitating network (FIN) for closed-form temporal feature learning over a 300ms signal window on Ninapro DB2, and applying it to the task of 17 hand movement recognition. We implement a lightweight LSTM-FIN network to imitate four standard temporal features (entropy, root mean square, variance, simple square integral). We then explore transfer learning capabilities by applying the pre-trained LSTM-FIN for tuning to a downstream hand movement recognition task. We observed that the LSTM network can achieve up to 99\% R2 accuracy in feature reconstruction and 80\% accuracy in hand movement recognition. Our results also showed that the model can be robustly applied for both within- and cross-subject movement recognition, as well as simulated low-latency environments. Overall, our work demonstrates the potential of the FIN modeling paradigm in data-scarce scenarios for sEMG signal processing.

Related papers

• FORS-EMG: A Novel sEMG Dataset for Hand Gesture Recognition Across Multiple Forearm Orientations [1.444899524297657]
  Surface electromy (sEMG) signal holds great potential in the research fields of gesture recognition and the development of robust prosthetic hands. The sEMG signal is compromised with physiological or dynamic factors such as forearm orientations, forearm displacement, limb position, etc. In this paper, we have proposed a dataset of electrode sEMG signals to evaluate common daily living hand gestures performed with three forearm orientations.
  arXiv  Detail & Related papers  (2024-09-03T14:23:06Z)
• BiLSTM and Attention-Based Modulation Classification of Realistic Wireless Signals [2.0650230600617534]
  The proposed model exploits multiple representations of the wireless signal as inputs to the network. An attention layer is used after the BiLSTM layer to emphasize the important temporal features. The experimental results on the recent and realistic RML22 dataset demonstrate the superior performance of the proposed model with an accuracy up to around 99%.
  arXiv  Detail & Related papers  (2024-08-14T01:17:19Z)
• EMGTFNet: Fuzzy Vision Transformer to decode Upperlimb sEMG signals for Hand Gestures Recognition [0.1611401281366893]
  We propose a Vision Transformer (ViT) based architecture with a Fuzzy Neural Block (FNB) called EMGTFNet to perform Hand Gesture Recognition. The accuracy of the proposed model is tested using the publicly available NinaPro database consisting of 49 different hand gestures.
  arXiv  Detail & Related papers  (2023-09-23T18:55:26Z)
• Light-weighted CNN-Attention based architecture for Hand Gesture Recognition via ElectroMyography [19.51045409936039]
  We propose a light-weighted hybrid architecture (HDCAM) based on Convolutional Neural Network (CNN) and attention mechanism. The proposed HDCAM model with 58,441 parameters reached a new state-of-the-art (SOTA) performance with 82.91% and 81.28% accuracy on window sizes of 300 ms and 200 ms for classifying 17 hand gestures.
  arXiv  Detail & Related papers  (2022-10-27T02:12:07Z)
• Time-to-Green predictions for fully-actuated signal control systems with supervised learning [56.66331540599836]
  This paper proposes a time series prediction framework using aggregated traffic signal and loop detector data. We utilize state-of-the-art machine learning models to predict future signal phases' duration. Results based on an empirical data set from a fully-actuated signal control system in Zurich, Switzerland, show that machine learning models outperform conventional prediction methods.
  arXiv  Detail & Related papers  (2022-08-24T07:50:43Z)
• Towards Domain-Independent and Real-Time Gesture Recognition Using mmWave Signal [11.76969975145963]
  DI-Gesture is a domain-independent and real-time mmWave gesture recognition system. In real-time scenario, the accuracy of DI-Gesutre reaches over 97% with average inference time of 2.87ms.
  arXiv  Detail & Related papers  (2021-11-11T13:28:28Z)
• Sign Language Recognition via Skeleton-Aware Multi-Model Ensemble [71.97020373520922]
  Sign language is commonly used by deaf or mute people to communicate. We propose a novel Multi-modal Framework with a Global Ensemble Model (GEM) for isolated Sign Language Recognition ( SLR) Our proposed SAM- SLR-v2 framework is exceedingly effective and achieves state-of-the-art performance with significant margins.
  arXiv  Detail & Related papers  (2021-10-12T16:57:18Z)
• 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)
• Video-based Remote Physiological Measurement via Cross-verified Feature Disentangling [121.50704279659253]
  We propose a cross-verified feature disentangling strategy to disentangle the physiological features with non-physiological representations. We then use the distilled physiological features for robust multi-task physiological measurements. The disentangled features are finally used for the joint prediction of multiple physiological signals like average HR values and r signals.
  arXiv  Detail & Related papers  (2020-07-16T09:39:17Z)
• Object Tracking through Residual and Dense LSTMs [67.98948222599849]
  Deep learning-based trackers based on LSTMs (Long Short-Term Memory) recurrent neural networks have emerged as a powerful alternative. DenseLSTMs outperform Residual and regular LSTM, and offer a higher resilience to nuisances. Our case study supports the adoption of residual-based RNNs for enhancing the robustness of other trackers.
  arXiv  Detail & Related papers  (2020-06-22T08:20:17Z)

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.