# Medical Gesture Recognition Method Based on Improved Lightweight Network
## 1 Introduction  
Medical assistant robots have made a significant impact in the operating room due to their dexterity and intelligence [1]. Due to the efforts of many academic institutions and leading surgical robot companies in medical robotics, medical assistant robots applied in different scenarios have gradually emerged [2]. In recent years, the demand for medical assistant robots is increasing rapidly due to the overload problem in the health care system caused by population growth [3]. We propose a medical assistant robot applied in the operating room for collaborative surgery through the information of the doctor's gestures. The doctor only needs to place one hand on the recognition platform of the medical assistant robot, and the robot will start to detect the gesture category. When there is no change within one second, the robot will deliver the surgical instrument corresponding to the recognition result to the doctor's hand, according to the prescribed route. The doctor can get the instrument with one hand and continue the surgery, and the robot starts waiting for new gesture input. The gesture recognition technology allows robots to replace nurses for medical mechanical delivery, not only improving the efficiency of medical treatment and saving healthcare resources, but also providing a sterile environment, thus avoiding the spread of infection [4]. Despite the rapid and accurate recognition of various gestures made by doctors, the existing technologies still face many challenges [5]. Gesture recognition technology has been a hot research topic in human-machine interaction. Gesture communication can provide a more natural and efficient interaction experience because of its advantages such as easy expression, rich information, and no contact [6]. Gesture recognition methods based on machine learning mainly rely on manual feature extraction and gesture segmentation [7]. [PERSON] et al. [8] used Gabor feature extractor and SVM classifier for the gesture recognition task. [PERSON] et al. [9] proposed a skin threshold segmentation method to recognize and classify gestures by segmenting the hand region in the image and extracting color and edge features from it using SVM classifier. Gesture recognition methods based on machine learning generally need to extract features before classification, which not only has complex operations but also low efficiency and poor generalization ability [10]. In recent years, deep neural networks have achieved great success in the field of image classification, and gesture recognition based on deep learning has become the mainstream direction [11]. Convolutional neural networks in deep learning can learn the features of input information layer by layer and classify it more accurately [12]. [PERSON] et al. [13] first performed a binarization preprocessing operation on the image, then set a threshold to localize the gesture, and finally used a convolutional neural network to recognize the gesture. [PERSON] et al. [14] proposed a CNN-based gesture recognition system, a deep convolutional generative adversarial network (DCGAN), to solve the overfitting problem. They used fewer samples for training and achieved better results. [PERSON] et al. [15] fused the collected image and radar information into the convolutional neural network for gesture classification. [PERSON] et al. [16] sent normalized images into a 3D convolutional neural network for gesture recognition, and the recognition rate reached 90% on average. [PERSON] et al. [17] proposed a new 2D convolutional neural network model (IJD) for gesture recognition. [PERSON] et al. [18] used Inception-v1 of GoogLeNet [19] to extract millions of weakly labeled data from sign language videos and input them into HMM to obtain gesture recognition results. In order to improve the recognition accuracy of deep neural networks, researchers deepened the depth of the model while making it more and more complex.