School of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China.
Sensors (Basel). 2024 Aug 20;24(16):5379. doi: 10.3390/s24165379.
The excessive use of electronic devices for prolonged periods has led to problems such as neck pain and pressure injury in sedentary people. If not detected and corrected early, these issues can cause serious risks to physical health. Detectors for generic objects cannot adequately capture such subtle neck behaviors, resulting in missed detections. In this paper, we explore a deep learning-based solution for detecting abnormal behavior of the neck and propose a model called NABNet that combines object detection based on YOLOv5s with pose estimation based on Lightweight OpenPose. NABNet extracts the detailed behavior characteristics of the neck from global to local and detects abnormal behavior by analyzing the angle of the data. We deployed NABNet on the cloud and edge devices to achieve remote monitoring and abnormal behavior alarms. Finally, we applied the resulting NABNet-based IoT system for abnormal behavior detection in order to evaluate its effectiveness. The experimental results show that our system can effectively detect abnormal neck behavior and raise alarms on the cloud platform, with the highest accuracy reaching 94.13%.
长时间过度使用电子设备会导致久坐人群出现颈部疼痛和压力损伤等问题。如果这些问题不能及早发现和纠正，可能会对身体健康造成严重风险。通用物体探测器无法充分捕捉到这些细微的颈部行为，从而导致漏检。在本文中，我们探索了一种基于深度学习的颈部异常行为检测方法，并提出了一种名为 NABNet 的模型，该模型结合了基于 YOLOv5s 的目标检测和基于轻量化 OpenPose 的姿态估计。NABNet 从全局到局部提取颈部的详细行为特征，并通过分析数据角度来检测异常行为。我们将 NABNet 部署在云端和边缘设备上，以实现远程监控和异常行为报警。最后，我们应用基于 NABNet 的物联网系统进行异常行为检测，以评估其有效性。实验结果表明，我们的系统可以有效地检测异常颈部行为，并在云平台上发出警报，最高准确率达到 94.13%。
