For the problem of surface electromyography (sEMG) gesture recognition, considering the fact that the traditional machine learning model is susceptible to the sEMG feature extraction method, it is difficult to distinguish the subtle differences between similar gestures. The NinaPro DB1 dataset is used as the research object, and the sEMG feature image and the Convolutional Neural Network (CNN) are combined to recognize 52 gesture movements. The CNN model effectively solves the limitations of traditional machine learning in sEMG gesture recognition, and combines 1-dim convolution kernel to extract deep abstract features to improve the recognition effect. Finally, the simulation experiment shows that compared with the accuracy of the raw-sEMG images based on the CNN and the sEMG-feature-images based on the CNN and sEMG based on the traditional machine learning, the multi-sEMG-features image based on the CNN is the highest, which coming up to 82.54%.

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Machine vision technology has been widely studied and applied in the development of science and technology, and has achieved remarkable achievements in real-time monitoring. Aiming at the upper limit of construction workers’ personnel, there are safety hazards in the unclosed elevators. The development of the machine vision number statistics is discussed in detail, mainly from the status quo of the population statistics research, the direct method of the population statistics and the application of the indirect method. According to different algorithm research, the problems existing in the current research direction are analyzed, and the number of people in the future elevators is forecasted.