## 1 Introduction  
As an important research topic in ubiquitous computing, Human Activity Recognition (HAR) from wearable sensors is a key element of various intelligent applications, such as smart personal assistants [1], healthcare assessment [2, 3, 4, 5, 6], sports monitoring [7], and aging care [8]. HAR can recognize a variety of individual behaviors, such as running, and walking, and has a wide range of applications [9]. HAR mainly uses wearable devices to collect individual behavior data and model the data for individual behavior identification. Deep neural networks perform well on HAR due to the ability to extract explicit feature design. Activity recognition models based on neural network technology often consist of three modules, which are the convolution module for extracting the correlation between sensor data, the convolution module for extracting the correlation among different kinds of sensor data, and the recurrent neural network module of extracting the correlation of data in a different moment. The models use convolution networks for feature extraction in the spatial dimension and then use recurrent neural networks to play for feature extraction in the temporal dimension [10]. In addition, some models also use the Attention mechanism to further enhance the generalization of the model [11]. However, there are two major deficiencies in the current datasets used to train activity recognition models in the real world: First, sensor data and activity labels are mostly collected using special experimental equipment in a supervised experimental environment, which affects the normal life of users [12; 13; 14]. Second, the modalities of sensor data are relatively single [15; 16], and existing studies focus on single or a few modalities of sensor readings, which neglects useful information and its relations existing in multimodal sensor data. Aiming at the two deficiencies of existing datasets, we build an experimental platform for activity recognition data collection and activity label collection: MarSense. MarSense can automatically collect a large amount of sensor data through a small number of user operations in the user's daily life. This data collection method takes full advantage of the powerful functions and widespread availability of smartphones, reduces the cost of data collection, and can collect richer data from a larger population. We also build our activity recognition model Marfusion, and use the sensor data and labels collected by the MarSense experimental platform for user experiments to train the model. Marfusion is to extract features in each dimension and the associations between different features to extract hidden features. It extracts features from multimodal sensors by the Convolution Neural Network (CNN) structure and Attention mechanism. For each sensor, a set of CNN-based networks is used for feature extraction for each modality. After that, Dot-Product Scaled Self Attention is used to process and give weight to each feature. Then, the data were multiplied with corresponding weights and they were input into the fusion feature extraction module. Finally, the data were input into the classification subnet to obtain the probability values of different categories. This paper also compares the training results of the Marfusion model with several other representative models. The value of Precision, Recall, and F1 reach 0.944, 0.944, and 0.943, which are higher than other models and verify the advantages of the Marfusion model compared to existing models in activity recognition. The rest of the paper is organized as follows. In the next Section 2, we introduce the existing approaches of HAR, HAR in the wild, and Data Fusion. Then, in Section 3 we introduce Marfusion and explain the principle and mechanism of vital modules. The data collection is presented in Section 5. In this section, we introduce our collection experiment, data collection platform (MarSense), and dataset construction. Section 6 presents the model training and model evaluation.