Design Of Intelligent Bionic Hand Based On Image Recognition

With the increasing functional requirements of patients with upper limb dysfunction in China,high-performance bionic hands have become one of the research hotspots.However,the current bionic hands have problems such as low pattern recognition control accuracy,less structural freedom,and insufficient flexibility.Aiming at the existing problems,this paper proposed a multi-degree-of-freedom bionic hand controlled by image recognition technology,and studied the structure design and control system of the bionic hand.First,this paper designed a bionic hand with 5 fingers and a wrist with reference to the physiological structure parameters of a normal human hand.It adopted the double transmission mode of tendon rope and connecting rod,and was equipped with 8 driving servos for coordinated control,which can realize five-finger grasping,three-finger grasping,two-finger pinch,inter-finger grasping and side pinch action.The D-H parameter analysis method was used to analyze the movement of the finger structure,and the results were compared with the ADAMS simulation results.The results showed that the mechanical structure of the bionic hand meets the requirements.At the same time,the relevant important structures were analyzed in the ANSYS WORKBENCH software,and the optimal structural design parameters were optimized to ensure the overall structural stability.According to its control characteristics,the overall control circuit of the bionic hand was designed,and the PCA9685 module was used to distribute the PWM wave signal with the same amplitude and frequency and multiple channels to complete the coordinated control of the overall structure of the bionic hand.Then,this paper build an image recognition neural network model with the help of convolutional neural network,and used QT cross-platform technology to develop an intelligent software with image acquisition and video recording functions,using this software to collect 36371 images for the model training in this paper data.The simplified version structure of the complete network model was used to analyze the influence of RGB image data and Gray image data on the recognition accuracy respectively,and the Gray image data was determined as the training data set of the network model in this paper.The initial training accuracy of the model was 71.02%.In order to improve the prediction accuracy of the neural network,this paper added deformable convolution to the residual structure of the neural network,and the recognition accuracy was increased to 84.98%,an overall increase of 13.96%.Finally,this paper made the overall control system experimental device and developed a bionic hand device.At the same time,an intelligent interactive platform was developed using Pyqt technology,which had the functions of image recognition process visualization and serial communication.This experimental platform was used to verify various functions of the bionic hand,and the feasibility of its image recognition function and the grasping function of the bionic hand were confirmed.The stability of the system provides a reference for the subsequent systematic research of the bionic hand.