# HP-YOLO: A Lightweight Real-Time Human Pose Estimation Method
## 1 Introduction
Third, there is the demand for lightweight models for resource-constrained devices: Despite the impressive strides made in Human Pose Estimation (HPE) across diverse applications, its translation to bedside patient monitoring encounters a unique set of obstacles [13]. Unlike open-environment scenarios where HPE models typically excel, the controlled yet complex environment of a hospital bedside presents significant challenges. Patients in these settings are rarely in unobstructed, standardized poses. Instead, their postures are often dictated by medical conditions, comfort needs, and the physical constraints inherent to the bedside environment: beds themselves, layers of bedding, and an array of medical equipment all contribute to occlusions, atypical viewpoints, and significant pose variations. Consequently, HPE algorithms trained on conventional datasets often struggle to generalize effectively to these uncharacteristic and challenging bedside poses [14]. This is particularly evident when considering pose variability. While HPE models are increasingly robust to pose changes within typical human movement ranges, the extreme and often unpredictable postures adopted by bedridden patients from fully supine to tightly curled push the boundaries of current HPE capabilities, resulting in reduced accuracy and reliability [15]. To tackle the challenge of computational constraints, we employ the OKS-Guided L1 Pruning Strategy. To effectively address these three aforementioned challenges, large pose variations, multi-scale feature representation, and computational constraints, we introduce a novel bedside HPE framework incorporating three key innovations in model design, with each innovation directly targeting one specific challenge:  
1. ELSKA Attention Mechanism for Handling Pose Variations: We introduce the ELSKA mechanism, strategically integrated within the SPPF feature extraction pyramid of the YOLOv8 backbone. This innovation fundamentally optimizes the network's architecture, endowing it with superior spatial context awareness and an enhanced effective receptive field. ELSKA leverages spatial attention for selective focus, thus enhancing the effective receptive field. ELSKA facilitates enhanced extraction of fine-grained features, directly addressing the challenges posed by significant pose variations. As explained above, the ELSKA mechanism is specifically designed to enhance the model's robustness to diverse and unpredictable patient poses by improving spatial context awareness and enhancing the effective receptive field. It is important to note that ELSKA primarily focuses on broadening the effective receptive field in the region in the input image that truly influences the network's output rather than solely expanding the theoretical receptive field defined by the network architecture. 2. RepNCSPELAN4 Structure for Multi-Scale Feature Fusion: In the model's head structure, we replace the conventional C2f module with the RepNCSPELAN4 (Reparameterized Non-Cross Stage Partial Efficient Layer Aggregation Network v4) structure. This architectural modification significantly amplifies the model's feature extraction prowess and its adaptability to multi-scale scenarios. RepNCSPELAN4 effectively mitigates the issues of insufficient large-scale information capture and inadequate feature fusion. As mentioned earlier, the RepNCSPELAN4 structure is specifically implemented to enhance the model's ability to capture and fuse multi-scale features, enabling it to effectively handle the scale variations inherent in bedside images. 3. OKS-Guided L1 Pruning Strategy for Real-Time Performance on Resource-Constrained Devices: To ensure real-time operation and compatibility with resource-limited devices, we employ a novel L1-norm based pruning strategy, intelligently guided by the Object Keypoint Similarity (OKS) metric. This pruning approach judiciously reduces computational complexity and storage footprint while preserving high model accuracy and guaranteeing real-time inference speeds, crucial for practical bedside deployment.