## 5 Implementation and Evaluation
### Data Collection  
PicoScene is a Wi-Fi sensing platform that provides high-quality and synchronized channel state information data from Wi-Fi devices. PicoScene is designed to collect and process large-scale Wi-Fi CSI datasets for various research purposes such as human activity recognition, localization, and wireless communication system optimization. Additionally, when compared to other existing tools like the Linux CSI Tool and Atheros CSI Tool, PicoScene has several advantages. Firstly, it supports a wide range of Wi-Fi chipsets and provides a higher sampling rate (up to 40 MHz) and higher resolution (up to 14 bits). This leads to more accurate and detailed CSI measurements. Additionally, PicoScene provides an easy-to-use and customizable software interface for data collection and processing, which can be adapted to different research scenarios. Lastly, PicoScene offers a powerful and flexible platform for Wi-Fi sensing and provides significant advantages over other tools for collecting Wi-Fi CSI data. After collecting Wi-Fi CSI data, CSI data provide information about the signal's amplitude and phase, which can be used for human activity recognition and localization. The amplitude and phase information of the CSI can be used to extract features, which can be used to distinguish between different human activities. For instance, Figure 4 shows that the amplitude and phase of the CSI can be used to detect the reflection of the signal from a human body to represent the sitting activity in location 4 performed by one volunteer. The reflected signal's characteristics vary depending on the human's body shape, size, and motion, which can be used to distinguish between different activities. Additionally, the phase information can be used to estimate the distance and direction of the human body from the Wi-Fi access point, which can be used for localization. To use amplitude and phase information for human activity recognition and localization, CSI data are first collected and preprocessed. Then, features are extracted using signal processing and deeplearning techniques. Finally, the extracted features are used to train a deep learning model for activity recognition and localization. Additionally, we visualize variations in the signals to represent different activities in the same locations. For instance, changes in signal strength, phase, and frequency can be used to differentiate between different activities or movements in the same location. Figure 5 demonstrates four different activities, including bending, sitting, standing, and walking, performed in the same location, carried out by plotting the amplitude values of the Wi-Fi signal over time. Each activity has a unique signal variation pattern that can be visualized as a waveform. For example, when a person is walking, there is periodic variation in the amplitude values of the Wi-Fi signal that can be observed in the waveform shown in Figure 5, upper right corner. Similarly, when a person is bending, there is a distinctive pattern of signal variation that can be visualized in the waveform, as shown in Figure 5, upper left corner. By analyzing the unique signal variation patterns of each activity, deep learning algorithms can be trained to recognize and classify different activities performed in the same location using Wi-Fi signals. Figure 4: CSI amplitude and phase information for the sitting activity in location 4. Figure 5: CSI amplitude sample of different activities in the same location. Similarly, Figure 6 visualizes variations in the signals to represent different activities in the same locations by plotting the phase values of the Wi-Fi signal over time. Each activity has a unique pattern of signal variation that can be visualized as a waveform. For example, when a person is sitting, there is periodic variation in the phase values of the Wi-Fi signal, which can be observed in the waveform shown in the bottom left corner.