## 2 Related Work  
Recently, computer vision applied to image-based HAR has made significant progress. [PERSON] et al. [7]. have provided valuable insights into visual semantic analysis using deep neural networks and load analysis through sensors. This is noteworthy for harnessing computer vision and sensor load for human behavior recognition. However, visual-based HAR is susceptible to environmental factors such as lighting and angle variations. Therefore, the application of low-cost sensor-based HAR methods has become more prominent in this field. [PERSON] et al. [8]. proposed three comprehensive algorithms for low-cost variable stiffness robotics mechanisms. These algorithms enhance recognition accuracy and reduce costs in the adjustment of robot joint stiffness. In [9], an innovative approach was introduced to address the estimation of external forces and torques on elastic joints. This method leverages the inherent elastic characteristics of joints, eliminating the need for expensive sensors. These approaches provide some cost-effective solutions for joint sensor design in HAR. When utilizing sensors for HAR, achieving a sufficient level of accuracy is crucial [10]. In scenarios where only a single sensor is employed for recognition, [PERSON] et al. developed a HAR system [11], in which individual IMUs are attached to the dominant wrist. They employed a variational autoencoder to automatically denoise the IMU signals, enhancing recognition accuracy. This approach achieved a recognition accuracy of 95.09% in everyday smartphone-based motion recognition. However, relying solely on sensors worn on the wrist has limitations when it comes to recognizing complex human gait behaviors. The choice of sensor placement significantly impacts the accuracy of human gait recognition. [PERSON] et al. devised a sensor selection approach using the feature selection criteria of maximum relevance and minimum redundancy. This method identifies optimal sensor placement for gait recognition [12]. Additionally, they implemented a two-layer classifier to differentiate interfered activities and incorporated physical features into the feature dictionary, thereby enhancing recognition accuracy. Through collecting data using sensors placed at appropriate positions, neural network methods have demonstrated strong recognition capabilities when processing such data, [PERSON] et al. presented an approach for recognizing hand activities in daily life [13]. They proposed a deep autoencoder based on ARMA (Auto-Regressive Moving Average) and a deep recursive network using GRU (Gated Recurrent Unit). The ARMA-based deep autoencoder effectively denoises the unprocessed time-series signals, while the deep RNN-GRU utilizes the output from the encoder to identify seven hand gestures. This approach yielded a 12.8% increase in accuracy compared to traditional classifiers for gesture recognition. Nevertheless, effective preprocessing techniques for sensor data remain essential to further enhance recognition accuracy. Applying data preprocessing and model pretraining techniques often leads to remarkable results in dealing with complex human activity recognition tasks. [PERSON] et al. proposed a method to transform raw accelerometer and gyroscope sensor data into the visual domain [14]. To address the issue of high computational complexity during this process, they employed fine-tuning through pretraining a CNN and transfer learning. On several online human activity recognition datasets, they achieved a classification accuracy of 98.36%. [PERSON] [15] combined data preprocessing techniques with the primary domain features of human activities, such as time, frequency, wavelet, and time-frequency features. They employed a random forest classifier to monitor human body activities. Results on five commonly used HAR datasets demonstrated that their method exhibited a certain superiority compared to cutting-edge approaches. However, the challenge lies in how to process and fuse data from multiple sensors to achieve accurate human behavior recognition.