# Development of a Chinese Chess Robotic System for the Elderly Using Convolutional Neural Networks
## 1 Introduction
Most of the recognition and segmentation work has involved hybrid methods, such as object recognition, human tracking, activity recognition, and human gait [28; 29; 30]. Meanwhile, [30] used depth image for face recognition, [26] adopted a similar technique but applied to time attendance systems, and [31] proposed  
Figure 1: Chinese chess. high multiplexing system performance to support face recognition over Wi-Fi. Action recognition, proposed by [32; 33; 34; 35], is more complicated and can be solved using an RGB-D camera with intrinsic features. However, the whole process of identification used a fixed orientation. It is very diverse from Chinese chess, where the position of a round piece can change its orientation when picked by a gripper so that it will become an obstacle during recognizing. For this reason, in recognizing targets, approaches such as a human way of thinking are needed. Previously, the artificial neural network (ANN) is a model developed based on imitating the structure and operation of the brain and becomes the basis of the convolution network. This method can be used to simulate complex models and prediction problems. The traditional neural network consists of three parts: the input layer, the hidden layer, and the output layer. The hidden layer has many neurons, and each neuron in each layer is connected to all neurons in the next layer. A network with multiple hidden layers is called a multilayer perceptron (MLP) [36]. For computer vision, a convolutional neural network (CNN) [37] is mainly used for image classification and object recognition. The main difference between an MLP and CNN is that only the last layer of a CNN is fully connected, while in an MLP, each neuron is connected to each neuron of the next layer, resulting in a large increase in the number of parameters. For large images, it generates complex vectors. In addition, it ignores spatial information and flattens the image as input. [PERSON] et al. [38] used CNNs to recognize traffic signs and used hinge loss stochastic gradient descent to train CNNs which were evaluated on the German traffic sign recognition benchmark. [PERSON] et al. [39] presented a hybrid deep convolutional neural networks (HDNNs) to recognize vehicles in satellite images by dividing the maps of the last convolutional layer and the max-pooling layer of DNN into multiple blocks of variable receptive field sizes or max-pooling field sizes to enable the HDNN to extract variable-scale features. In addition to images, CNNs are also used for speech recognition. [PERSON] et al. [40] used a limited-weight-sharing scheme to simulate speech features in CNNs. Compared with DNNs, the bit error rate of the proposed method is reduced by 6-10%. In this paper, the chess piece is photographed by a camera and the picture is input to a convolutional neural network (CNN) for chess recognition. At the same time, the coordinates of the chessman are obtained by image processing and sent to a robot system to grab the target chessman using the forward and inverse dynamics. In this paper, the CNN will be used to recognize the characters on the chessmen and distinguish the front or backside of the chessmen, even when they are randomly placed. The robot arm will be controlled to accurately grasp the chessmen and place them on exact positions of the chessboard. The remainder of this paper is organized as follows: the Chinese chess robotic system is introduced in Section 2 and the convolutional neural network is indicated in Section 3. Experimental results are analyzed in Section 4 along with the conclusions in Section 5 of the paper.