# HP-YOLO: A Lightweight Real-Time Human Pose Estimation Method
## 4 Experiment
### Experimental Results and Analysis  
To comprehensively evaluate the performance of our HP-YOLO model, we conducted comparative experiments with several state-of-the-art human pose estimation methods, including lightweight OpenPose, the HRNet series, and the latest SRPose model. All models were evaluated on the COCO validation set under identical conditions, without the use of pre-trained weights. The experimental results are summarized in Table 3.  
The results in Table 3 clearly demonstrate that while SRPose [28] achieves an AP score of 48.4 with a parameter size of 23.5 M, it incurs a significant computational overhead. In contrast, our HP-YOLO model, with only 4.08 M parameters, achieves a higher AP score of 53.4 and an AP\({}^{50}\) score of 82.0, demonstrating both superior detection accuracy and computational efficiency. Specifically, compared to other lightweight models, such as lightweight OpenPose [26] and EfficientHR-H3 [21], HP-YOLO shows a notable improvement. Our HP-YOLO achieves an AP that is 10.6% higher than lightweight OpenPose and 8.6% higher than EfficientHR-H3, highlighting its superior performance in pose estimation tasks while maintaining a compact model size. Furthermore, we evaluated the robustness of HP-YOLO in challenging scenarios from the COCO validation set, such as single-person, multi-person, partial occlusion, and crowded environments. As illustrated in Figure 4, we present a visual comparison of YOLOv8n and HP-YOLO's performance across increasingly complex scenarios from the COCO validation set. From left to right, the columns represent scenarios of increasing difficulty: single-person, single-person with complex background, multi-person, partial human body (occlusion), and multi-person with complex background. In each scenario, the traditional YOLOv8n algorithm exhibits limitations, such as inaccurate pose estimation or missed detections, while HP-YOLO consistently demonstrates improved accuracy and robustness by effectively reducing these errors. This visual evidence highlights HP-YOLO's enhanced ability to handle diverse and challenging real-world scenarios compared to the baseline model. Finally, to assess the generalizability of HP-YOLO in real-world clinical settings, we conducted bedside pose estimation experiments involving complex postures and occlusions. Figure 5 visually compares the performance of the baseline YOLOv8 model (top row) and our proposed HP-YOLO model (bottom row) under identical challenging bedside scenarios. Figure 5 highlights HP-YOLO's practical advantages in clinical applications, demonstrating superior bedside pose estimation. Specifically, Figure 5a, illustrating 'Leg Joint Misalignment', shows the baseline model inaccurately localizing distal leg and foot joints, resulting in misalignment, while HP-YOLO accurately captures the posture. Figure 5b, depicting 'Partial Missed Detection' in a 'Cluttered Background', reveals the baseline model's failure to fully detect the person amidst clutter, leading to incomplete pose estimation. Figure 5c, highlighting 'Complex Posture Missed Detection + Misalignment', demonstrates the baseline model's struggle with complex poses and occlusion, resulting in both joint misdetection and misalignment, indicating limited robustness in such scenarios. Error Cause Analysis: Observed limitations of the baseline YOLOv8 in both COCO (Figure 4) and bedside scenarios (Figure 5) indicate its vulnerability to complex visual environments, clutter, occlusion, and unusual poses. HP-YOLO's enhanced architecture, particularly the ELSKA and REPNCSPELAN4 modules, effectively addresses these challenges. These modules likely improve feature extraction, contextual understanding, and robustness to visual variations, enabling HP-YOLO's superior performance in both general and application-specific challenging scenarios, thus demonstrating its practical value for robust human pose estimation. Across all cases, HP-YOLO demonstrates superior accuracy and robustness compared to the baseline model, making it well suited for clinical applications.