## 3 Proposed System Model
### Real-Time Data Collection and Integration  
This phase involves the real-time collection of data from environmental sensors and user feedback, followed by the integration of these data into a centralized platform. The sensors continuously monitor environmental parameters, providing a continuous data stream, while user feedback is collected and integrated with sensor data, making it suitable for subsequent analytical processes within the system. As shown in Figure 2, this study is designed to monitor and analyze the user experience in a library by integrating environmental sensors and visual perception technologies. The ESP WROOM 32 microcontroller (F), sourced from Espressif Systems, located in Shanghai, China, collects environmental data through the following sensors: the MQ135 sensor (B), sourced from Hanwei Electronics, located in Zhengzhou, China, which measures air quality (CO\({}_{2}\), CO, ammonia, and other gases); the LDR sensor (C), sourced from Adafruit Industries, located in New York, NY, USA, which detects light intensity; the HTU21D sensor (A), sourced from TE Connectivity, located in Schaffhausen, Switzerland, which records the temperature and humidity levels; the DFR0034 sensor (D), sourced from DFRobot,  
Figure 1: Visual representation of environmental data collection, processing, and analysis process. located in Beijing, China, which detects sound levels; and the SGP30 sensor (E), sourced from Sensirion, located in Stafa, Switzerland, which measures TVOC and eCO\({}_{2}\) levels. The collected environmental data are transmitted to a central computer via Wi-Fi using the MQTT protocol. By utilizing a Raspberry Pi 4B (H) sourced from Raspberry Pi Foundation, located in Cambridge, UK, and Raspberry Pi Camera (G), curved from Raspberry Pi Foundation, located in Cambridge, UK, the number of people in the library is monitored in real time, and crowd density analysis is performed. Camera data are processed with the OpenCV library (version 4.5.3) and synchronized with the environmental data. This enables a more comprehensive examination of the effects of environmental factors on the user experience. The data are transmitted via Wi-Fi and saved as timestamped CSV files on the central computer (I), while Python 3.11 is employed for data recording and analysis. The entire system is powered by a portable power bank, making it mobile and adaptable for use in different environments. Additionally, user feedback is gathered via a QR code (J) linked to a Google Forms survey (K), which is stored on Google Sheets and synchronized with the environmental data based on timestamps. This method facilitates a more detailed analysis of the impact of environmental factors on the user experience. The data collection process was carried out in the main reading hall of Duzce University Library, selected due to its users' sensitivity to environmental factors and the high traffic in the library. During the dataset creation phase, environmental data, visual perception data, and user feedback were integrated by aligning them at specific time intervals. The data obtained from the sensors were transmitted via Wi-Fi and stored as timestamped CSV files on the central computer. These data were then synchronized with the user feedback collected from the surveys and aligned based on timestamps to allow for an analysis of the impact of each environmental factor on the user experience. User feedback, gathered via the QR code,  
Figure 2: Visual process of dataset preparation by integrating environmental sensor data and user feedback. was stored concurrently with the environmental sensor data, resulting in a comprehensive dataset. The user feedback was categorized into sections such as library usage habits, environmental conditions (noise, light, temperature, ventilation, crowd), and health and comfort status. This categorization enabled a more detailed investigation of the effects of environmental factors on the library experience.