## 4 HAR Datasets and Experiment Settings  
### Experimental Environment and Data Acquisition  
For human body pose recognition using IMU sensors, we have meticulously assembled a specialized hardware and software configuration. The system features an Intel Core i9-12900K CPU at 3.50 GHz and an Nvidia GeForce RTX 3080 Ti GPU with 12 GB VRAM, ensuring high-speed computation for accurate pose analysis. With 64 GB DDR4 3000 RAM, memory-intensive operations are efficiently handled. TensorFlow 2.5 serves as the deep learning framework, supported by Python 3.9 as the primary development language. PyCharm 2020.1 IDE offers a user-friendly platform for coding and debugging. Windows 10 Professional OS provides stability and reliability for precise pose recognition. This setup optimizes IMU sensor capabilities, benefiting sports biomechanics, healthcare, and animation applications. The relevant configurations of the experiment are shown in Table 1.  
Sensor-based human activity recognition is a sophisticated technology that utilizes sensor technology to monitor and analyze the spatial positioning and motion characteristics of the human body. It automatically recognizes and interprets various human activities, making it applicable in diverse scenarios. We employ an inertial measurement unit (IMU) sensor technology to achieve a more comprehensive and in-depth recognition and analysis of human activity information. The datasets used in the experiment are the PAMAP2 dataset and the MultiportGAM dataset, where the PAMAP2 dataset is a public dataset that can be publicly obtained. The MultiportGAM dataset is our self-made dataset, which includes 19 different types of activities and generates a total of 11,034 sample data. We invited 10 volunteers to collect sensor data for the MultiportGAM dataset. To ensure the accurate collection of human activity data, we utilized specific sensors for different body parts. An accelerometer is employed to measure the acceleration of the human torso, left hand, right hand, left leg, and right leg. Additionally, a magnetometer is used to measure the magnetic field strength and direction of these body parts. Furthermore, a gyroscope is utilized to measure the angular velocity of the aforementioned body segments. The eight types of daily activities collected are shown in Figure 8. By compensating for the data from the magnetometer and gyroscope, more precise localization of human joints is achieved, enhancing the accuracy of human activity identification. The sampling frequency of the sensor is 30 Hz, and the number of collected action types is 19. Each volunteer completed these actions within 1-2 h, with an average duration of 3-5 min for each action. In order to keep the behavioral data collected by sensors closer to the real-world human behavior, some specific actions such as cycling, shooting, paddling, etc., in Figure 8 are collected using sensors with the help of relevant equipment. Daily actions such as standing, sitting, walking, going upstairs, lying down, etc., are carried out in an orderly manner under indoor conditions to ensure the stability of the collection environment and avoid magnetic field interference. In addition, each row of data includes sensors for calibration to eliminate bias and improve measurement \begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Specific Configuration** \\ \hline CPU & Intel Core [EMAIL_ADDRESS] GHZ \\ Graphics card & Nvidia GeForce RTX 3080 Ti (12 GB) GPU \\ Memory & 64 GB DDR4 3000 \\ Deep learning framework & TensorFlow 2.5 \\ Development language & Python 3.9 \\ Developing an IDE & Pycharm 2020.1 \\ Operating system & Window10 Professional \\ \hline \hline \end{tabular}
\end{table} 
Table 1: High-performance AI workstation for advanced deep learning applications. accuracy. The collected raw action data includes action labels and timestamps for subsequent data analysis and alignment. Table 2 records the basic information of the two datasets, including activity category, sample size, and sensor type.