Grasping is a challenging problem in robotics and prosthetic applications due to its control requirements. The visual perception and analyzing electromyography (EMG) signals are the two ways to give the inputs to robots and prosthetic amputees for grasping abilities. The EMG is a diagnostic manner that evaluates the fitness condition of skeletal muscles. Examination or evaluation of the EMG signals is time-consuming and arduous for experts. Hence, the state-of-the-art methods in artificial intelligence (AI) is employed to improve the accuracy rate for the detection and classification of EMG signals for grasping. Recently, deep learning architectures have been used in many engineering applications such as diagnosis of health conditions, computer vision, and human machine interaction (HMI). In this study, a new deep one-dimensional convolutional neural network model (1D-CNN) is proposed to classify six types of hand movements. Our proposed 1D-CNN model implemented using surface EMG (sEMG) has obtained the highest accuracy of 94.94% in classifying six hand movements. The strength of our model is that, it can perform the automated classification of various hard grasps using only one channel data. Our developed prototype model is ready to be tested with more data and can be used to assist in musculoskeletal disorders.

由于其控制要求，在机器人技术和假肢应用中，抓取是一个具有挑战性的问题。视觉感知和肌电图分析（EMG）信号是向机器人和假肢截肢者提供输入以掌握能力的两种方式。EMG是一种评估骨骼肌健康状况的诊断方法。对EMG信号的检查或评估对专家来说既费时又费力。因此，采用了人工智能（AI）的最新方法来提高对EMG信号进行检测和分类以进行抓取的准确率。最近，深度学习架构已用于许多工程应用中，例如健康状况的诊断，计算机视觉和人机交互（HMI）。在这项研究中，六种类型的手部动作。我们提出的使用表面肌电图（sEMG）实施的1D-CNN模型在对六个手部运动进行分类时获得了94.94％的最高准确度。我们的模型的优势在于，它仅使用一个通道数据就可以对各种难题进行自动分类。我们开发的原型模型已准备就绪，可以用更多数据进行测试，并且可以用于协助肌肉骨骼疾病。