Mani Ruth Keziah, Nagaraj Balakrishnan
Department of Biotechnology, Rathinam Technical Campus, Coimbatore, India.
Department of Electronics and Communication Engineering, Rathinam Technical Campus, Coimbatore, India.
Sci Rep. 2025 Jun 2;15(1):19274. doi: 10.1038/s41598-025-03766-2.
Hand recognition, the process of identifying or characterizing human hands in images or video streams, plays significant role in the biometrics, robotics, computer vision, and human-computer interaction. This technology relies on analyzing hand attributes such as shape, size, color, texture, and motion to perform tasks as gesture identification, hand tracking, and sign language interpretation. In particular, hand movement decoding from electromyography (EMG) signals has shown promise for understanding neuromuscular function and aiding in diagnosis and therapy for neuromuscular issues. Existing approaches range from deep learning techniques such as Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) to conventional machine learning methods like Support Vector Machines (SVM) and Random Forest. Deep learning automates the process, reducing the dependency on manual feature extraction. However, the performance of these models is heavily influenced by hyperparameters such as the number of neurons, hidden layers, and learning rates. This study proposes a novel method that uses the Grey Wolf Optimizer (GWO) to fine-tune the hyperparameters of a Bi-LSTM-based EMG classification system. Implemented in MATLAB R2021a, this approach aims to enhance the accuracy of Bi-LSTM models in categorizing EMG signals. Performance metrics such as accuracy of 95%, precision of 93%, F1-score of 94%, and recall of 91% are used for thorough evaluation. By leveraging GWO for hyperparameter optimization, the study aims to achieve more accurate diagnosis and efficient tracking of rehabilitation outcomes for patients with neuromuscular disorders. This research demonstrates the potential of integrating biomedical engineering and computational intelligence to empower individuals with neuromuscular disabilities, thereby enhancing their quality of life.
手部识别是指在图像或视频流中识别或表征人类手部的过程，在生物识别、机器人技术、计算机视觉和人机交互中发挥着重要作用。这项技术依靠分析手部的属性，如形状、大小、颜色、纹理和运动，来执行诸如手势识别、手部跟踪和手语解读等任务。特别是，从肌电图（EMG）信号中解码手部运动，对于理解神经肌肉功能以及辅助神经肌肉问题的诊断和治疗已显示出前景。现有的方法从深度学习技术，如长短期记忆（LSTM）和双向长短期记忆（Bi-LSTM），到传统的机器学习方法，如支持向量机（SVM）和随机森林。深度学习使这个过程自动化，减少了对手动特征提取的依赖。然而，这些模型的性能在很大程度上受到超参数的影响，如神经元数量、隐藏层和学习率。本研究提出了一种新颖的方法，使用灰狼优化器（GWO）来微调基于Bi-LSTM的EMG分类系统的超参数。该方法在MATLAB R2021a中实现，旨在提高Bi-LSTM模型在对EMG信号进行分类时的准确性。使用诸如95%的准确率、93%的精确率、94% 的F1分数和91%的召回率等性能指标进行全面评估。通过利用GWO进行超参数优化，该研究旨在实现对神经肌肉疾病患者更准确的诊断和对康复结果的有效跟踪。这项研究证明了整合生物医学工程和计算智能以赋能神经肌肉残疾个体的潜力，从而提高他们的生活质量。
