Towards safer environments: A YOLO and MediaPipe-based human fall detection system with alert automation

IF 1.9 Q2 MULTIDISCIPLINARY SCIENCES
MethodsX Pub Date : 2025-09-11 DOI:10.1016/j.mex.2025.103623
Virag Pradip Kothari , Priti S. Chakurkar
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引用次数: 0

Abstract

Detecting human falls is essential to ensuring public safety, particularly in public areas like transit terminals. This study provides a precise and effective real-time fall detection system by utilising pose estimation and deep learning-based object detection approaches. The system correctly detects falls in dynamic circumstances by combining MediaPipe's pose estimate for in-depth body posture analysis with the YOLOv8 model for human recognition. The paper provides a novel method that improves the system's scalability and robustness in real-world scenarios by utilising position landmarks and activity identification algorithms. To enable accurate fall detection and reduce false positives, the system also uses anomaly detection techniques. The system uses Twilio to send real-time warnings as soon as a fall is detected, send out video footage of the incident, and alert the appropriate authorities. The system is an excellent option for enhancing safety in sizable public areas because to its effectiveness, scalability, and privacy-preserving features. Metrics like accuracy, precision, recall, and F1-score are used in the study to assess the system's performance and show its usefulness. The system outperformed existing fall detection approaches, achieving 96.06 % accuracy and 100 % recall, confirming its robustness in real-world scenarios.

Abstract Image

迈向更安全的环境:基于YOLO和mediapie的自动报警人体跌倒检测系统
检测人体跌倒对于确保公共安全至关重要,特别是在交通枢纽等公共区域。本研究通过利用姿态估计和基于深度学习的物体检测方法,提供了一个精确有效的实时跌倒检测系统。该系统通过将MediaPipe的深度身体姿势分析的姿势估计与用于人类识别的YOLOv8模型相结合,正确检测动态环境中的跌倒。本文提供了一种新的方法,通过利用位置地标和活动识别算法,提高了系统在现实场景中的可扩展性和鲁棒性。为了实现准确的跌落检测并减少误报,系统还使用了异常检测技术。该系统使用Twilio,一旦检测到摔倒,就会发送实时警告,发送事件的视频片段,并提醒有关当局。由于其有效性、可扩展性和隐私保护特性,该系统是提高大型公共区域安全性的绝佳选择。研究中使用了诸如准确性、精确度、召回率和f1分数等指标来评估系统的性能并显示其有用性。该系统优于现有的跌倒检测方法,准确率达到96.06%,召回率达到100%,证实了其在现实场景中的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
MethodsX
MethodsX Health Professions-Medical Laboratory Technology
CiteScore
3.60
自引率
5.30%
发文量
314
审稿时长
7 weeks
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