## 1 Introduction  
The rapid progress of the Internet of Things (IOT) has accelerated the applications of human-computer interaction (HCI) [1] and gestures, such as in enabling smart homes, VR, as well as elderly care management. Therefore, gesture recognition in the simplest and most efficient ways has become a significant research direction in this present era. To date, the mainstream approaches to gesture recognition include gesture recognition based on wearable devices [2, 3], gesture recognition based on computer vision [4], and gesture recognition based on RFID [5]. Although wearable-based gesture recognition has been studied in recent years with high recognition accuracy, a device plastered on human body undeniably causes inconvenience to the users at the extent of affecting one's interaction experience. Meanwhile, computer vision-based gesture recognition approach demands the use of a camera [6], which denotes privacy violation, vision blockage in specific areas, and the requirement for adequate lighting at the target region. Turning to gesture recognition based on Wi-Fi and radio frequency (RF) approaches, there is neither privacy violation, a wearable device on the body, nor lighting demand. Real-time gesture recognition based on Wi-Fi and RF signals via RFID has garnered popularity within the academic research area [7, 8]. The RFID-based recognition of human gestures studied by [PERSON] et al. [9] yielded good results, despite the strict requirements for its signal characteristics in tagging location and notable unsatisfactory scenarios when applied in the actual setting. The Wi-Fi-based gesture recognition system is classified based on received signal strength (RSSI), phase, and amplitude. Among them, RSSI (coarse-grained signal) displays poor stability performance in the environment of signal interference and indoor multipath. As for the phase-based gesture recognition, [PERSON] et al. [9] reported good results despite the intricate processing. Meanwhile, channel state information (CSI)-based gesture recognition has garnered much attention due to its high sensitivity towards changes in action (fine-grained signal), high diversity in both frequency and spatial domains, as well as possession of more information than RSSI. [PERSON] et al. [10] proposed Widar 3.0 to capture and estimate the velocity profile of gestures at a lower signal level, which only required training of the classifier once to identify recognition of gestures in various domains. However, the complexity of the algorithm for feature extraction disrupted real-time computation. Next, [PERSON] et al. [11] proposed a cross-domain-based gesture recognition using deep neural network (DNN) and reported good experimental results. However, the use of deep learning method to classify and recognize gestures required huge amount of dataset for training, while the use of additional hardware resources led to complex models and lengthy training time. Such complex deep learning models could easily cause an overfitting issue. In the attempt to address the problems stated above, an easy-to-implement and resource-efficient method for gesture recognition is proposed in this study. In doing so, the extracted CSI amplitude information was deployed in a series of processing to classify the gestures using a public dataset [12]. The contributions of this study are listed as follows:  
1. Original information extracted from complex environment, while omitted user's gesture direction and velocity vector, Hampel, discrete wavelet pre-processing, and principal component analysis (PCA) were employed to select the optimal subcarrier for feature extraction and selection. 2. For the selection of antenna, this paper is the first to use signal-to-noise ratio (\(S/N\)), variance size, as well as amplitude maximum and minimum differences, as the indicators. 3.