## 1 Introduction  
Human activity recognition (HAR) has become an essential domain in the age of ubiquitous computing and intelligent appliances. The capacity to autonomously identify and categorize individuals' behaviors employing data from wearable sensors has significant ramifications across several sectors, including medical services, intelligent residences, fitness monitoring, and individualized solutions [1, 2, 3]. With the increasing popularity of electronic devices, including advanced sensors, the possibility for more accurate and contextually aware HAR systems also rises. Machine learning techniques have transformed HAR, providing robust instruments for deriving significant trends from sensor data. Traditional machine learning techniques, such as support vector machines and random forests, are often employed because they are easy to use and interpret [4]. In recent years, deep learning methodologies, including convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, have attained the highest possible efficiency by autonomously acquiring hierarchical representationsof features [5]. These models effectively incorporate temporal and spatial correlations in sensor data, enhancing accuracy across different tasks. The increasing complexity of deep learning architectures introduces significant challenges, particularly in interpretability and explainability. While these models often achieve high accuracy, their internal decision-making processes remain primarily opaque, leading to the well-known black-box problem [6]. This lack of transparency may erode user trust, hinder regulatory adherence, and impede the successful deployment of HAR systems in critical applications. Recent improvements in attention mechanisms, such as squeeze-and-excitation (SE) networks [7], have shown promise in improving model efficacy by allowing focus on the most important elements [8], yet their application in HAR, particularly with explainability approaches, requires further investigation. This study tackles these challenges by proposing an efficient deep residual neural network model incorporating the SE mechanism. Our approach utilizes data from wearable smart device sensors to improve movement tracking accuracy and provide us with a way to comprehend how the model makes decisions. The main contributions and novelties of this study are as follows:  
1. This study introduces an efficient deep residual neural network model, ConvResBiGRU-SE (Convolutional Residual Bidirectional Gated Recurrent Unit with SE), a novel integration of CNNs, residual connections, bidirectional gated recurrent units (GRUs), and SE mechanisms specifically optimized for HAR tasks. 2. This research develops an explainable framework for our HAR model, allowing a transparent interpretation of the model's predictions. Unlike many deep learning HAR systems that function as black boxes, our approach incorporates gradient-weighted class activation mapping (Grad-CAM) visualizations specifically adapted for time-series sensor data. This addresses a critical gap in the literature by making complex deep-learning models for HAR more transparent and interpretable. 3. This investigation uniquely evaluates performance across multiple datasets with different sensor placements (waist-mounted in UCI-HAR vs. front pocket in WISDM), demonstrating the architecture's robustness to sensor positioning variations--a practical challenge rarely addressed in previous studies. 4. Our systematic ablation experiments quantify the specific contribution of each architectural component. This methodical approach reveals that the SE mechanism improves model stability (reducing standard deviation by over 50%) while enhancing accuracy--insights not previously established in the HAR literature. The remainder of this paper is structured to the following: Section 2 examines relevant literature in HAR, deep learning techniques, and attention mechanisms. Section 3 delineates the suggested deep learning framework and its constituents.