Wang Yanchao, Tian Ye, She Haotian, Jiang Yinlai, Yokoi Hiroshi, Liu Yunhui
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Beijing Advanced Innovation Center for Intelligent Robot and System, Beijing 100081, China.
Micromachines (Basel). 2022 Jan 29;13(2):219. doi: 10.3390/mi13020219.
In this paper, we develop a prosthetic bionic hand system to realize adaptive gripping with two closed-loop control loops by using a linear discriminant analysis algorithm (LDA). The prosthetic hand contains five fingers and each finger is driven by a linear servo motor. When grasping objects, four fingers except the thumb would adjust automatically and bend with an appropriate gesture, while the thumb is stretched and bent by the linear servo motor. Since the change of the surface electromechanical signal (sEMG) occurs before human movement, the recognition of sEMG signal with LDA algorithm can help to obtain people's action intention in advance, and then timely send control instructions to assist people to grasp. For activity intention recognition, we extract three features, Variance (VAR), Root Mean Square (RMS) and Minimum (MIN) for recognition. As the results show, it can achieve an average accuracy of 96.59%. This helps our system perform well for disabilities to grasp objects of different sizes and shapes adaptively. Finally, a test of the people with disabilities grasping 15 objects of different sizes and shapes was carried out and achieved good experimental results.
在本文中，我们开发了一种假肢仿生手系统，通过使用线性判别分析算法（LDA）实现具有两个闭环控制回路的自适应抓握。该假肢手包含五个手指，每个手指由一个线性伺服电机驱动。抓握物体时，除拇指外的四个手指会自动调整并以适当的姿势弯曲，而拇指则由线性伺服电机伸展和弯曲。由于表面肌电信号（sEMG）的变化发生在人体运动之前，使用LDA算法识别sEMG信号有助于提前获取人的动作意图，然后及时发送控制指令以协助人们抓握。对于活动意图识别，我们提取了三个特征，即方差（VAR）、均方根（RMS）和最小值（MIN）进行识别。结果表明，其平均准确率可达96.59%。这有助于我们的系统为残疾人自适应抓握不同尺寸和形状的物体提供良好性能。最后，对残疾人抓握15个不同尺寸和形状物体进行了测试，并取得了良好的实验结果。
