## 2 Related Works and Our Contributions  
### Related Works  
HAR plays a critical role in Intelligent Transportation Systems (ITS), enhancing safety and transportation efficiency. Recent studies emphasize the role of physiological, inertial, and environmental sensors--particularly smartphones and wearable devices--in advancing HAR [14]. This research focuses on HAR using multiple smartphone sensors to address both accuracy and applicability. [PERSON] et al. [15] demonstrated that integrating gyroscope and accelerometer data improves classification accuracy by 2%, particularly for static activities, but limited improvement was noted for dynamic activities such as walking and running. [PERSON] et al. [16] conducted a review indicating that, while accelerometers alone generally outperform gyroscopes, their combined usage improves overall accuracy by approximately 10%, highlighting the impact of sensor placement and orientation. [PERSON] et al. [17] emphasized that additional sensors, like gravity and linear accelerometers, could enhance HAR accuracy but noted that current datasets often fail to represent real-world variations in sensor positioning. Advantages and Limitations: These studies highlight that while sensor fusion improves HAR, the focus on controlled and position-dependent datasets limits broader applicability. Our study addresses these limitations by using a comprehensive sensor suite--including accelerometers, gyroscopes, linear accelerometers, and gravity sensors --positioned more realistically, reflecting everyday user scenarios. [PERSON] et al. [18] introduced a contrastive fusion learning approach specifically designed for multimodal HAR with limited data, which demonstrates improved adaptability across various sensor inputs. However, this approach is primarily designed for controlled environments with limited data, lacking extensive testing on position-independent configurations, which our study aims to address by incorporating a broader range of smartphone-based sensors. Additionally, [PERSON] et al. [19] proposed a Transportation Mode Recognition (TMR) approach using both acceleration and location data, achieving an F1 score of 91%. Their model performed slightly better (92.3%) under varied smartphone placements but focused mainly on transportation modes and did not evaluate the potential advantages of other sensor types. [PERSON] et al. [20] reviewed human activity recognition methods using smartphone-based sensors, suggesting their effectiveness in data capture but did not explore the value of adding sensors like linear accelerometers or gravity sensors for enhanced recognition capabilities. In comparison, ShoesLoc by [PERSON] et al. [21] utilized in-shoe force sensors for indoor walking path tracking with high accuracy in path estimation. This hardware-specific approach is effective but lacks general applicability in broader HAR scenarios, unlike smartphone-based HAR systems such as ours, which do not require specialized equipment. Challenges and Our Approach: These findings underscore the need for HAR approaches that incorporate diverse sensors beyond basic accelerometers and gyroscopes. Our research addresses these gaps by including a combination of sensors, which we found to slightly improve activity classification across a range of realistic, uncontrolled scenarios. CrossHAR by [PERSON] et al. [22] employed a hierarchical self-supervised pretraining strategy to enhance cross-dataset generalization in HAR, achieving strong generalization across diverse HAR datasets. While CrossHAR aims to improve adaptability across datasets, our work complements this goal by evaluating HAR in real-world, position-independent smartphone placements, providing robustness in uncontrolled environments. Previous research largely relies on fixed sensor positions for high accuracy. For example, [PERSON] et al. [23] combined smartphone and IMU sensors at fixed positions, achieving high recognition rates across various activities.