Yadav Mr Amol Pandurang, Patil Dr Sandip R
All India Shri Shivaji Memorial Society's Institute Of Information Technology, India.
Bharati Vidyapeeth's College Of Engineering for Women, Pune, India.
MethodsX. 2025 Feb 15;14:103207. doi: 10.1016/j.mex.2025.103207. eCollection 2025 Jun.
This study presents a novel deep learning approach for surface electromyography (sEMG) gesture recognition using stacked autoencoder neural network (SAE)s. The method leverages hierarchical representation learning to extract meaningful features from raw sEMG signals, enhancing the precision and robustness of gesture classification.•Feature Extraction and Classification MODWT Decomposition: The sEMG signals were decomposed using the MODWT DECOMPOSITION(Maximal Overlap Discrete Wavelet Transform) to capture various frequency components.•Time Domain Parameters: A total of 28 features per subject were extracted from the time domain, including statistical and spectral features.•Classifier Evaluation: Initial evaluations involved Autoencoder and LDA (Linear Discriminant Analysis) classifiers, with Autoencoder achieving an average accuracy of 77.96 % ± 1.24, outperforming LDA's 65.36 % ± 1.09.Advanced Neural Network Approach: Stacked Autoencoder Neural Network: To address challenges in distinguishing similar gestures within grasp groups, a Stacked Autoencoder Neural Network was employed. This advanced neural network architecture improved classification accuracy to over 100 %, demonstrating its effectiveness in handling complex gesture recognition tasks. These findings emphasize the significant potential of deep learning models in enhancing prosthetic control and rehabilitation technologies. . To verify these findings, we developed a 3d hand module in ADAMS software that is simulated using Matlab-ADAMS cosimulation.
本研究提出了一种使用堆叠自动编码器神经网络（SAE）进行表面肌电图（sEMG）手势识别的新型深度学习方法。该方法利用分层表示学习从原始sEMG信号中提取有意义的特征，提高手势分类的精度和鲁棒性。•特征提取与分类 最大重叠离散小波变换（MODWT）分解：使用最大重叠离散小波变换（MODWT）对sEMG信号进行分解，以捕获各种频率成分。•时域参数：从时域中提取每个受试者总共28个特征，包括统计和频谱特征。•分类器评估：初步评估涉及自动编码器和线性判别分析（LDA）分类器，自动编码器的平均准确率达到77.96%±1.24，优于LDA的65.36%±1.09。先进的神经网络方法：堆叠自动编码器神经网络：为了解决在抓握组内区分相似手势的挑战，采用了堆叠自动编码器神经网络。这种先进的神经网络架构将分类准确率提高到了100%以上，证明了其在处理复杂手势识别任务方面的有效性。这些发现强调了深度学习模型在增强假肢控制和康复技术方面的巨大潜力。为了验证这些发现，我们在ADAMS软件中开发了一个3D手部模块，并使用Matlab-ADAMS联合仿真进行模拟。
