Research And Analysis Of Tractor Head-up Display Interface Based On SEMG Signal And Virtual Simulatio

The rapid development of China’s rural economy has put forward higher requirements for agricultural machinery and equipment,especially the requirements for display systems in large agricultural equipment such as tractors are constantly increasing.In order to meet the new needs of the development of agricultural production,my country’s agricultural tractors are constantly developing in terms of scale,intelligence and high technology.However,the tractor has problems such as concentrated running time and long driving time,which continue to exist.Working in such an environment is likely to cause the tractor driver’s driving fatigue so that the driver’s driving safety cannot be effectively guaranteed.In order to improve the driving safety factor of the tractor driver,and to improve the problem that the tractor driver often turns his head to watch the operation of the agricultural machinery behind the tractor,which causes the driver to easily cause fatigue,damage to the cervical spine,and endanger the driver’s driving safety,this paper proposes a solution for the tractor cab.Use head-up display technology(HUD)to view the working situation of agricultural machinery behind the cab and conduct research and analysis.Based on sEMG surface electromyography theory,Jack man-machine virtual simulation,Deep Lab V3+ image segmentation algorithm,and eye-tracking technology as a theoretical basis and experimental methods,this paper provides reliable data support for research.This paper mainly studies the following contents:(1)The sEMG electromyographic signal instrument was used to collect the surface electromyographic signals of the tractor driver in the two driving postures of turning his head to watch the operation of the rear agricultural machinery and equipment and the front-facing posture,and the collected muscle signal data were analyzed by MATLAB software.Time-domain and frequency domain analysis and extraction of effective feature quantities for data comparison and analysis;combined with the ergonomics software Jack to conduct virtual human-machine simulation,import the 3D model of the tractor,establish a digital human with a percentile of 50,and virtualize the neck comfort Simulation analysis,combining the two fatigue analysis methods of sEMG signal and Jack virtual simulation,found that the tractor driver had a higher degree of comfort when driving the tractor in the head-up state,which provided a theoretical basis for subsequent research.(2)Collect the picture video data of the tractor driver observing the rear agricultural machinery and implements,and use the Deep Labv3+ image segmentation algorithm to process the collected video picture.In the experimental part,the Pytorch deep learning framework is used to build the required model network structure and complete the corresponding code development,and finally obtain a clearer segmentation image of the agricultural machinery and equipment operation picture.(3)Perform eye-tracking experiments on the original images of the agricultural machinery and tools that the tractor drivers need to watch and the images after semantic segmentation,and use eye-tracking software to extract the subjects’ effective eye movement index data and heat maps,and use SPSS.After analyzing the original data and drawing the conclusion of significance analysis,the study found that the subjects can better identify the key information in the image through the segmented image and reduce the cognitive load,which is beneficial for the driver to watch.