# BSTCA-HAR: Human Activity Recognition Model Based on Wearable Mobile Sensors
## 2 Related Work  
In recent years, HAR systems based on machine learning (ML) and deep learning (DL) have become increasingly common [11]. Traditional ML methods emphasize the feature engineering stage, in which key attributes are pinpointed and distilled to distinguish between diverse activity profiles and minimize reliance on manual preprocessing. Feature extraction from primitive data is crucial for the effective operation of HAR systems. These features are then used as inputs to a classifier for recognizing human behavior. Earlier studies used the discrete wavelet and Fourier transforms [12] to identify frequency-based characteristics, then ML methods such as decision trees, logistic regression, and random forest (RF) to classify the extracted features [13; 14]. While these methods can obtain better results on the HAR problem, they rely heavily on manual feature extraction. This process is marked by its time-consuming and labor-intensive nature while also being crucially contingent upon the excellence of feature extraction, factors which can easily lead to unstable model performance. DL techniques have been extensively employed within the realm of HAR. Their attractiveness mainly lies in their ability to automatically extract features without human intervention. Studies have shown that recurrent neural network (RNN)-based models can be successfully applied to the problem of human behavior and activity recognition [15],effectively recognizing various human activities such as walking and running. However, the high complexity of RNN models makes it difficult to effectively capture long-term dependencies. In order to increase the convergence speed of the loss function, scholars have proposed many RNN improvement methods to optimize model performance [16]. Subsequently, convolutional neural networks (CNN) have excelled in feature extraction. [PERSON] et al. [17] achieved remarkable success in medical image analysis using a CNN, particularly in the classification of Alzheimer's disease. Analogous to our approach in the HAR system, where a CNN is utilized for feature extraction and classification, converting raw sensor data into high-level abstract sequences addresses the issue of manual feature extraction in traditional human activity recognition [18]. [PERSON] et al. [19] converted trivial accelerometer data into an \"image\" format, with the convolutional layer extracting deep spatial features from action video frames; however, its ability to capture temporal information was limited, and it had difficulty dealing with complex temporal dependencies. In addition, some previous models have utilized transformer-like methods to handle irregularly sampled time series [20], as in [PERSON] et al. [21], who utilized a self-attention mechanism in their model for improved human behavior recognition accuracy obtained better performance than traditional methods. However, these methods are deficient in capturing temporal information, and cannot effectively handle time series data. To enhance the model's ability to capture features at different time scales, researchers have proposed methods that incorporate multiple DL techniques. [PERSON] et al. [22] demonstrated that an architecture combining CNN and LSTM with an attention mechanism excels in handling HAR tasks. Their model performed well in multi-scale feature extraction and key feature recognition. [PERSON] et al. [23] constructed an end-to-end model that integrates the CNN, autoencoder (AE), and LSTM architectures, enabling efficient HAR operations without the need for intricate data preprocessing. Furthermore, in [24] they proposed a method for hemiplegic gait prediction based on smartphone sensor data, combining both manually designed and automatically extracted features. However, when dealing with multi-class tasks and detecting intricately detailed movements, their system still faces challenges such as structural complexity and high computational costs. To address the aforementioned limitations, we propose a novel human activity recognition model called BSTCA-HAR.