## 3 Deep Learning-Based Action Recognition  
Many researchers are interested in and conducting deep learning-based action recognition research. Approximately 25 papers have been submitted to this special issue, and 12 of them were accepted (i.e., 34.2% acceptance rate). This special issue mainly consists of training data, pose estimation of objects, action recognition and gesture recognition. [PERSON] et al. [5] present an approach on how to solve a data shortage problem in deep learning, by extracting synthesized acceleration and gyro norms data from video for human activity recognition scenarios.  
There are two papers focused on pose estimation--the first one by [PERSON] and [PERSON] introduces the Lightweight Stacked Hourglass Network [6], which expands the convolutional receptive field while also reducing the computational load and providing scale invariance. The second paper, authored by [PERSON] and [PERSON] [7], proposes a Partition Pose Representation, which integrates the instance of a person and their body joints based on joint offset. They also propose a Partitioned Center Pose Network, which can detect people and their body joints simultaneously, then group all body joints.  
Four papers deal with action recognition directly by using convolutional networks. The first paper, authored by [PERSON] et al. [8], introduces high-order spatial and temporal features of skeleton data, such as velocity, acceleration, and relative distance, to construct graph convolutional networks. The other three papers adapt the spatio-temporal concept to extract better features. [PERSON] et al. [9] suggest a spatio-temporal image formation technique of 3D skeleton joints by capturing spatial information and temporal changes for action discrimination. [PERSON] and [PERSON] [10] proposes a low-cost embedded model to extract spatial feature maps by applying CNN to the images that develop the video and using the frame change rate of sequential images as temporal information. The low complexity was achieved by transforming the weighted spatial feature maps into spatio-temporal features, and then inputting the spatio-temporal features into multilayer perceptrons. [PERSON] et al. [11] propose an improved Long Short-Term Memory (LSTM) network, which is able to extract time information. They enhanced the input differential feature module and spatial memory state differential module to enhance features of actions. [PERSON] et al. [12] introduce the concept of class regularization, which regularizes feature map activations based on the classes of the examples used. The proposed method essentially amplifies or suppresses activations based on an educated guess of the given class.  
There are four papers focused on gesture recognition. Gesture recognition generally consists of a series of continuous actions, so it is necessary to memorize past actions. Four papers independently propose a unique method for gesture recognition. [PERSON] et al. [13] present a dynamic gesture recognition approach using multi-features extracted from RGB frame and 3D skeleton joint information. [PERSON] et al. [14] exploit depth and skeletal data for the dynamic hand gesture recognition problem. The paper also explores a multi-level feature LSTM with pyramid and the LSTM block, which deal with the diversity of hand features. [PERSON] et al. [15] present a neural network for sensor-based hand gesture recognition, which is extended from the PairNet. [PERSON] et al. [16] present another dynamic hand gesture recognition approach with two modules: gesture spotting and gesture classification, which uses bidirectional LSTM and a single LSTM, respectively.