NABNet: Deep Learning-Based IoT Alert System for Detection of Abnormal Neck Behavior.
Sensors (Basel)
; 24(16)2024 Aug 20.
Article
em En
| MEDLINE
| ID: mdl-39205072
ABSTRACT
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%.
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Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Aprendizado Profundo
/
Pescoço
Limite:
Humans
Idioma:
En
Revista:
Sensors (Basel)
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
China
País de publicação:
Suíça