<p>Wearable sensor-based human activity recognition (HAR) methods utilize multimodal data from sensors such as accelerometers, gyroscopes, and magnetometers to infer human activities. Recent advancements employ deep learning techniques, leveraging deep neural networks to automate feature extraction for activity classification. However, existing deep hybrid networks face challenges in distinguishing between similar activities and handling complex scenarios, particularly when integrating data from diverse sensor types. To address these limitations, this study introduces a novel hybrid deep learning architecture, termed MultiLevel Convolutional Transformer (MLConvTrans), aimed at enhancing the efficiency and accuracy of sensor-based HAR systems. MLConvTrans integrates two core components: the Multi-Level Convolutional Network (MLConvNet) and the n-stacked Transformer Encoder (TransEncoder). MLConvNet captures local features from individual sensors and performs multimodal fusion to obtain comprehensive global feature representations, effectively extracting both low-level and high-level information. These features are then processed by the TransEncoder block, which integrates global features and models long-term dependencies for activity classification. The proposed architecture is extensively validated on multiple public datasets, including UCI-HAR, MotionSense, HAPT, KU-HAR, SHL2018, and PAMAP2. Experimental results demonstrate that MLConvTrans significantly outperforms state-of-the-art methods in wearable sensor-based HAR while maintaining computational efficiency.</p>