## 3 Methods
### GAM-MLP Information Fusion  
#### 3.4.1 Human Pose Feature Information Fusion  
In order to better process the spatiotemporal features of IMU sensor data, the neural network model that is suggested in this paper incorporates information fusion [25], attention evaluation [26], and classification recognition [27]. Figure 4 depicts the model architecture of GAM-MLP and the process of action classification. The data of different sensors in each part of the human body are put into the GAM-MLP model as one-dimensional features to recognize human activities, and finally the recognition results are obtained. The data format represented by (, 64) in the figure represents (batch_size, data dimension), due to batch_size is not fixed and is therefore represented as blank. GAM-MLP's primary objective is to extract posture features of the utmost significance for action recognition from a 20-dimensional dataset. This extraction process is intended to facilitate effective information fusion and utilize these features as the primary foundation for classification. To accomplish this objective, the paper introduces a component called the GAM. In conventional neural network classification methodologies, each pose feature is uniformly trained, with their significance determined solely by the weights assigned to individual neurons. However, this method suffers from inadequate communication among distinct pose features, ultimately yielding less impactful results. This paper introduces a novel approach that combines manual partitioning and automatic learning. This hybrid approach empowers the neural network model to allocate varying degrees of importance to different categories of pose features, ultimately resulting in an enhanced classification accuracy. In the GAM, the pose features are manually categorized into distinct groups. During the manual grouping process, following feature extraction via FFT, it becomes evident that there exist resemblances in the characteristics among features associated with various  
Figure 3: The processing graph for FFT feature extraction. activities. For instance, the frequency features of \"Sitting\" and \"Standing\" are characterized by frequencies of less than 4 Hz and exhibit a unimodal pattern. On the other hand, activities such as \"Exercising on a stepper\" and \"Playing Basketball\" demonstrate characteristic frequencies reaching approximately 10 Hz, marked by multiple peaks and substantial fluctuations. Consequently, human motion activities can be effectively classified into dynamic and static groups. The static group conforms to low-frequency attributes with a single wave peak, while the dynamic group encompasses high-frequency elements, multiple peaks, and significant fluctuations. As depicted in the accompanying Figure 5, we provide a visual comparison of feature images extracted from several activity features. Furthermore, adhering to the principles of the periodicity inherent in dynamic motion, the dynamic group can be further categorized into two subgroups: periodic dynamic activity and random dynamic activity. Within the periodic dynamic activity category, distinctions can be made based on the unique patterns and trends of peak characteristics. To summarize, the criteria and guidelines for manual partitioning can be systematically delineated, progressing from higher-order groupings to lower-order ones, in accordance with the specific characteristics of the dataset. The schematic representation of the group attention module is illustrated in Figure 6. The blocks with different colors in the figure represent the weights calculated by different groups. To address the inherent nonlinear relationships and patterns within both the dynamic and static groups of features, this paper introduces the \"Transform Block\", which combines elements from MLP and the Transformer model. Both MLP and the Transform Block are integral components of deep learning models, commonly employed in tandem to handle intricate datasets and tasks.