## 1 Introduction
In [9], a comprehensive study that quantitatively compares the performance of these diverse deep learning models using publicly available CSI datasets is conducted. This research highlights the capability of these models to discern subtle differences between input classes. However, a common challenge emerges. As the distinctions between input data from different classes become more minute, the sensing accuracy of these deep learning models tends to decline [10; 11]. The wireless signal propagation environment changes randomly over time and space. In addition, the hardware in the Wi-Fi transmitters and receivers are not perfect. Thus, the measured CSI values exhibit random fluctuations even when the monitoring area and conditions remain constant. The fluctuating nature of these measurements presents a substantial obstacle to achieving precise categorization with deep learning algorithms. To address the challenges posed by the CSI variability and improve the classification accuracy of a deep learning model, various CSI data representation methods have been proposed. These methods aim to enhance the quality of input data provided to a deep learning model by reducing noise and random fluctuations in the CSI measurements [12], or extracting more robust and informative features from the raw CSI data [13; 14]. These techniques consider rapidly changing components in the measured CSI values as noise and remove them to reduce variability in the data. In other words, they extract features that show less variation over time by assuming that these more stable features will be more informative for a deep learning model for classification. However, the extracted values often do not differ significantly for the number of people (i.e., class) in the monitored area. These subtle differences make it challenging for a deep learning model to accurately classify the number of people. To resolve the issue, in this paper, we propose a novel CSI feature representation method that can better capture the class-specific characteristics in CSI data while still mitigating the effects of noise and random fluctuations. As is noted in [15], it is very hard to ensure the existence of specific features performing better than others for all applications and scenarios. This is mainly attributed to the wide spectrum of the environments where Wi-Fi sensing systems operate. For example, the Wi-Fi signal transmission environment differs for each location where Wi-Fi sensing is applied. The strength of noise and interference at the moment of CSI measurement varies. Furthermore, the specifications, locations, and configurations of Wi-Fi sensing transmitters and receivers are different. Thus, our goal in this paper is not to find a panacea method that always gives the best performance for all classes in all Wi-Fi sensing environments but rather to find a method that performs well in most environments and for most classes. To achieve the goal, we propose a method to transform CSI amplitude data collected over a time interval into a color image by using the jet colormap [16]. Colormaps are important tools used in data visualization to represent data by converting them into colors. Various colormaps have been designed by considering various factors such as color theory, the human visual system, and the relationship between colors and data [17; 18]. Our purpose is not data visualization but to better distinguish CSIs belonging to different classes in a higher-dimensional space. Therefore, we use a colormap not as a tool to transform data into a form suitable for the human visual system without visual distortion in color changes but as a tool to amplify subtle differences in CSI data. The jet colormap is a rainbow color palette which can represent scalar data with uniform differences nonuniformly through nonlinear changes in brightness and color [19]. Therefore, when the jet colormap is used, data boundaries or features may appear more exaggerated than they actually are [20].