## 3 Proposed Methodology
### Data Pre-Processing  
#### 3.2.1 Data Denoising  
To effectively address the impact of noise on the CSI and overcome the potential lack of discernible characteristics for different activities, it is crucial to employ ML techniques for noise filtering and feature extraction. Various noise reduction techniques can be utilized, such as the implementation of Butterworth low-pass filters [34]. Nonetheless, the presence \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & **No. of Participants** & **Collection Tools** & **Bandwidth and** & **Activities** & **No. of Samples** \\  & **(Age Range)** & & **Number of Subcarries** & **Activities** & **No. of Samples** \\ \hline CSI-HAR & 3 (25 to 70 yrs) & Raspberry Pi-4B & 40 MHz and & Lie down & 405 \\  & & Nexmon CSI Tool & 52 Subcarriers & Fall & 437 \\  & & & Bend & 415 \\  & & & Run & 449 \\  & & & Sit down & 413 \\  & & & Stand up & 348 \\  & & & Walk & 398 \\ \hline StanWiFi & 6 (unidentified) & Intel 5300 NIC & 20 MHz and & Lie down & 657 \\  & & 30 Subcarriers & Fall & 443 \\  & & & Run & 1209 \\  & & & Sit down & 400 \\  & & & Stand up & 304 \\  & & & Walk & 1465 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the CSI datasets used in this study. of high-bandwidth burst and impulse noises in the CSI, using low-pass filters alone, is not feasible for achieving a seamless CSI stream. Based on empirical evidence, there are more effective approaches to achieve this objective, including employing principal component analysis (PCA) for noise denoising [34]. PCA is a method that reduces the complexity of a system by identifying key features where a significant portion of relevant information is concentrated. In the context of PCA-based denoising, this study adheres to the recommendation proposed in [34]. It involves excluding the initial principal component and instead selecting the subsequent five principal components for feature extraction. The reason behind this choice is that the noises arising from internal state transitions are present in all CSI streams. These noises, which show a strong correlation, get mixed into the initial principal component, along with the signal generated by human motion. However, it is important to note that all the human motion signal data present in the initial principal component are also captured within the remaining principal components. In the context of PCA, the components derived from PCA show no correlation with each other. As a result, the initial principal component solely represents one of these orthogonal components, while the rest are preserved within the subsequent PCA components. Therefore, removing the initial principal component does not compromise any relevant data. The decision to choose five principal components for feature extraction is supported by empirical evidence. The aim is to find a balance between classification effectiveness and computational overhead. To mitigate noise, the initial principal component is excluded, and the subsequent five components are utilized for feature extraction. This approach preserves data related to the dynamic reflection of the mobile target, as it is also captured in other primary components. Following the application of PCA denoising to the CSI data, specific characteristics are extracted to enhance its usability for classification purposes. To demonstrate the denoising performance, we compared the PCA denoising method using the Signal-to-Noise Ratio (SNR), which represents the ratio of signal power (meaningful information) to noise power. The denoising results are presented in Figures 2 and 3. Figure 2 illustrates the amplitude received from all subcarriers of CSI data obtained from the CSI-HAR dataset after noise reduction using the PCA denoising method. Notably, the denoised CSI signals exhibit higher SNR values compared to the raw CSI samples. These findings indicate the successful reduction of noise from the raw CSI data. Figure 3 presents the raw and smoothed CSI signals for six human activities from the StanWiFi dataset.