# HP-YOLO: A Lightweight Real-Time Human Pose Estimation Method
## 4 Experiment
### HP-YOLO Ablation Study  
To rigorously assess the effectiveness of our proposed enhancements, we conducted comprehensive ablation experiments on the COCO dataset, utilizing both YOLOv8n and YOLOv5 architectures. This study systematically deconstructs the individual contributions of the ELSKA attention module and the REPNCSPELAN4 module, and critically evaluates  
Figure 5: Visualcomparison of bedside pose estimation performance in challenging clinical scenarios. Black dashed boxes highlight error regions (misalignment or missed detections). (**a**) Leg Joint Misalignment; (**b**) Partial Missed Detection; and (**c**) Complex Posture (Missed Detection + Misalignment). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Params (M) \(\downarrow\)** & **GMACs \(\downarrow\)** & **AP (\%) \(\uparrow\)** & **AP\({}^{50}\) (\%) \(\uparrow\)** \\ \hline Lightweight OpenPose [26] & 4.1 & 18 & 42.8 & - \\ EfficientHR-H2 [21] & 10.3 & 15.4 & 52.9 & 80.5 \\ EfficientHR-H3 [21] & 6.9 & 8.4 & 44.8 & 76.7 \\ EfficientHR-H4 [21] & 3.7 & 4.2 & 35.7 & 69.6 \\ LitePose-XS [27] & **1.7** & **1.2** & 40.6 & - \\ MFite-HRNet [9] & 1.8 & 2.43 & 41.4 & - \\ SRPose [28] & 23.5 & 30.86 & 48.4 & - \\ YOLOv5 [17] & 11.3 & 14.41 & 45.4 & 77.1 \\ YOLOv8n & 3.2 & 4.35 & 47.5 & 79.6 \\
**HP-YOLO (Ours)** & 4.08 & 5.7 & **53.4** & **82.0** \\
**P-HP-YOLO (Ours)** & 2.7 & 3.65 & 52.4 & 80.7 \\ \hline \hline \end{tabular} Note: Bold indicates the best performance in each column; “Ours” denotes our proposed methods. \(\downarrow\) indicates smaller-is-better metrics, \(\uparrow\) indicates larger-is-better metrics.  
\end{table} 
Table 3: COCO [32] Validation Set Results for Comparison (Average Over 5 Runs). the efficacy of ELSKA by benchmarking its performance against well-established attention mechanisms: CBAM [24] and SE [23] blocks. For the YOLOv5 model, integrating ELSKA yielded a 0.9% increment in AP and a 3.1% increment in APL, demonstrably enhancing large object detection. Substituting the C2f module with REPNCSPELAN4 independently improved AP by 3.5%. The synergistic combination of ELSKA and REPNCSPELAN4 culminated in a noteworthy AP of 50.5%. To contextualize ELSKA's performance, we benchmarked it against CBAM and SE, with results meticulously detailed in Table 4. ELSKA consistently surpassed these alternatives. Specifically, deploying ELSKA in isolation achieved 46.3% AP, a 0.5% advantage over CBAM (45.8%) and a substantial 1.8% advantage over SE (44.5%). The performance superiority of ELSKA was further accentuated when synergistically integrated with REPNCSPELAN4, attaining 50.5% AP, in contrast to 48.5% with CBAM and 47.3% with SE. Notably, isolated implementation of the SE block within YOLOv5 resulted in a marginal performance regression relative to the baseline, potentially indicating architectural incompatibility under such conditions. Conversely, ELSKA consistently delivered robust performance augmentations across all evaluated configurations within the YOLOv5 framework. Similarly, for the YOLOv8n model, ELSKA in isolation improved AP by 0.4%, while REPNCSPELAN4 independently contributed a 3.4% AP improvement. Their combined deployment achieved a culminating AP of 53.4%. Comparative evaluations against CBAM and SE on YOLOv8n unveiled a congruent trend. ELSKA, utilized alone, attained 47.9% AP, marginally outperforming CBAM (47.2%) and SE (46.6%). Conjointly employed with REPNCSPELAN4, ELSKA again reached the apex AP of 53.4%, exceeding CBAM+REPNCSPELAN4 (50.4%) and SE+REPNCSPELAN4 (49.2%). These findings collectively reinforce that while CBAM and SE offer incremental enhancements over the 'None' baseline in YOLOv8n, their performance gains remain consistently less pronounced than those afforded by ELSKA.