一种融合地标识别与深度学习技术的跌倒检测系统新设计
A new design of a fall detection system integrating landmark identification and deep learning techniques
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中文总结 AI 辅助
本研究提出一种结合地标识别与深度学习的跌倒检测系统，利用Media Pipe和预测算法减少误报，实验显示95%检测率，适用于老年护理与智能健康监测。
中文摘要 AI 辅助
本文介绍了一种创新系统，该系统将地标识别与深度学习相结合，以提高跌倒检测的准确性和可靠性。通过利用先进的计算机视觉技术，如用于空间识别的Media Pipe，该系统能有效区分日常动作与真实跌倒。地标与深度学习预测算法的集成最大限度地减少了误报，确保对真实跌倒做出及时响应。全面的实验强调了该系统在各种场景下的多功能性，凸显了其提高老年人安全性和独立性的潜力。训练过程显示准确率稳步提升，并在第40轮时趋于稳定，而错误率在初始轮次中显著下降。实时实验涉及年龄在8至50岁之间的男性和女性参与者，记录了高达95%的跌倒检测率，证明了该系统的有效性及其在老年护理和智能健康监测环境中的未来应用前景。
英文摘要
This article introduces an innovative system that integrates landmark identification with deep learning to enhance fall detection accuracy and reliability. By utilizing advanced computer vision techniques, such as Media Pipe for spatial recognition, the system effectively differentiates between routine movements and actual falls. The integration of landmarks with a deep learning prediction algorithm minimizes false alarms, ensuring timely responses to genuine falls. Comprehensive experimentation underscores the system's versatility across various scenarios, emphasizing its potential to improve safety and independence for older adults. The training process demonstrates a steady increase in accuracy, stabilizing by the 40th cycle, while error rates decline significantly during the initial cycles. Real-time experiments, involving both male and female participants aged 8 to 50, recorded a remarkable 95% detection rate of falls, demonstrating the system's effectiveness and promising future applications in elder care and smart health monitoring environments.
发表机构
- University of Information Technology, Vietnam National University in Ho Chi Minh City（胡志明市越南国家大学信息技术大学）
- Thu Dau Mot University（土龙木大学）
机构由 AI 辅助整理，请以论文原文为准。
