## 6 Conclusions and Future Work  
This paper offered a hybrid HAR approach using an MLP neural network and Euler angle extraction based on IMU sensors. In the method, by employing accelerometers, we precisely capture the acceleration of the torso, left arm, right arm, left leg, and right leg. This meticulous data collection approach is supplemented by gyroscopes, which monitor the angular velocity of various bodily components, and magnetometers, which gauge the direction and strength of magnetic fields. Refining joint placement through compensation of magnetometer and gyroscope data enhances the accuracy of human activity identification. Experimental findings, derived from tests conducted on widely used datasets, underscore the superiority of our proposed GAM-MLP model over existing deep learning-based models. On the PAMAP2 dataset and the MultiportGAM dataset, accuracy rates of 93.96% and 96.13% were achieved, respectively. Particularly noteworthy is the 97.45% accuracy achieved in recognizing six common daily activities within the MultiportGAM dataset. Compared to traditional methods and some advanced techniques, an improvement of 1.04% to 10.8% in accuracy was attained. Nevertheless, there is still room for improvement in GAM-MLP in identifying confusing actions  
Looking forward, our future endeavors are geared towards exploring the realm of online deep learning models and expanding the horizons of both human activity recognition (HAR) and GAM-MLP to adeptly handle vast volumes of multivariate data. Specifically, in the future, additional inference modules can be considered in the GAM-MLP network to accelerate recognition speed and accuracy. When dealing with multivariate data, increasing the depth of the network appropriately may improve the recognition effect. Furthermore, the attention mechanism is intricately connected to the challenges posed by big data. Big data often exhibits diversity and variability, and attention mechanisms can adapt to various data distributions and characteristics, thereby enabling models to better accommodate shifts in the data. We are committed to enhancing the robustness of attention mechanisms in HAR recognition. They can assist models in focusing on crucial information, suppressing noise and outliers, and dynamically adjusting weights based on changing data, thereby maintaining model stability within the context of big data.  
Conceptualization, L.Y. and Y.M.; methodology, H.G.; software, Y.M.; validation, Y.Y. and X.H.; formal analysis, Y.H.; investigation, L.Y.; resources, H.G.; data curation, L.Y.; writing--original draft preparation, Y.M.; writing--review and editing, X.H. and Y.Y.; visualization, H.G.; supervision, Y.H.; project administration, X.H.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Zhejiang Province (No. LGG21F010005) and the National Natural Science Foundation of China under grant No. 61602137 and 61702144.  
Not applicable.  
Written informed consent was obtained from the volunteer(s) to publish this paper.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.