## 1 Introduction  
With the continuous advancement of technology and the increasing focus on physical well-being, sensor-based human activity recognition (HAR) has gained significant attention and has been widely applied. Human activity refers to the motion status of the human body in space, encompassing its position, angles, and trajectories. The recognition of human activity holds great potential across diverse domains, including medical rehabilitation [1], sports science, human-computer interactions, and gaming. In these fields, the attainment of precise and real-time human activity recognition plays a pivotal role in attaining predefined objectives. The practical uses of traditional camera-based activity recognition systems are severely constrained by their low recognition accuracy, susceptibility to ambient interference, and high processing complexity. Contrarily, sensor-based human position identification techniques, such as those using depth cameras, audio signals, and inertial measurement units (IMUs), offer important benefits like high accuracy, low latency, and robustness to environmental factors [2]. These benefits support the quick advancement and extensive application of sensor-based activity recognition techniques. However, there also exist certain drawbacks to the sensor-centric approach. Firstly, sensor acquisition and integration amplify hardware costs and system intricacy, rendering it unsuitable for scenarios with constrained budgets or space requirements. Secondly, specific motions or actions might necessitate intricate algorithmic parsing, potentially leading to performance degradation. The processing and fusion of sensor data necessitate meticulous calibration and adjustments for varying sensor types, involving intricate technical challenges. Human activity recognition entails monitoring and analyzing the body's movement status through the utilization of sensors, and automatically recognizing activity based on spatial or trajectory characteristics. The versatility of this approach allows for its application in various scenarios, bringing with it distinct advantages and specific conditions for implementation. Furthermore, the fusion of multiple sensor technologies enables a more thorough and comprehensive recognition and analysis of human activity, providing valuable insights into activity-related information. For example, placing sensors on different parts of the body (such as the wrist, waist, and ankle) can obtain more comprehensive information about human activity, helping to identify more complex behaviors such as bending, turning, etc. Human activity recognition can be achieved through different sensor-based methods. Inertial measurement units (IMUs) calculate body movements based on acceleration, angular velocity, and magnetic flux data from key body areas like wrists and knees [3]. Depth cameras, using infrared encoding, accurately recognize whole-body activities and joints [4]. Video sequence technology analyzes high-speed camera footage for activity recognition in sports and medical rehabilitation. Acoustic signal analysis estimates body activity by analyzing sound signals in the context of human-machine interactions and virtual reality scenarios. These methods enhance human-computer interactions, optimize sports training, aid medical rehabilitation, and advance virtual reality applications. Although the IMU (inertial measurement unit) has been widely used in HAR, it still faces limitations in practical applications. The raw data from a single accelerometer is too simplistic to independently determine activity. Meanwhile, gyroscopes accumulate small errors over time with prolonged usage, leading the integrated values from the sensor to progressively deviate from true values. This phenomenon can result in the drift of integrated values over time, ultimately impacting the accuracy of applications [5]. As a result, many researchers have proposed multi-sensor fusion algorithms based on Kalman filters, combining attitude data from accelerometers, gyroscopes, and magnetometers.