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基于改进的深度残留收缩网络的视力障碍者盲道实时预警系统的研究

Zhezhou Yu^1,2, Fuwang Wang³

¹Guangdong Peizheng College, Guangzhou, 510830, China. zhezhou_yu@163.com.

Scientific reports

|April 29, 2025

概括

这项研究引入了一种实时预警系统,使用脑电图 (EEG) 信号来检测盲人道路上的视力受损个体的痛苦. 该系统达到96.72%的准确性,提高了安全性,并使得协助速度更快.

科学领域:

生物医学工程生物医学工程
人工智能的人工智能
辅助技术辅助技术辅助技术

背景情况:

视障人士在道路上面临重大导航挑战,包括断路和紧急情况.
现有的预警系统对于这些情况缺乏准确性和实时能力.

研究的目的:

为盲人道路上的交通堵塞开发一种针对视力受损者量身定制的新型实时预警系统.
分析电脑电图 (EEG) 信号,以确定危险级别和需要援助的情况.

主要方法:

提出了一个实时预警系统,分析电脑电图 (EEG) 信号以检测情绪状态 (正常,轻度焦虑,极度焦虑).
引入了基于密集块 (DB-DRSN) 的改进深度残留收缩网络,以处理复杂且可能杂的EEG数据.
在DB-DRSN内集成密集的连接,以增强从浅层和深层EEG信号层的特征提取.

主要成果:

DB-DRSN系统在识别视力障碍者所面临的困难方面取得了96.72%的高准确率.
拟议的系统在准确性和实时性能方面明显优于传统的预警模型.
与现有的预警方法相比,证明了更快的援助交付.

结论:

基于DB-DRSN的实时预警系统有效地检测并警告视力受损者关于盲目道路拥堵的情况.
该系统通过及时检测和干预,显著提高了视力受损者的安全性.
该研究强调了人工智能驱动的EEG分析在改善具有挑战性的环境中的可访问性和安全性的潜力.

关键词:

一个盲目的道路被困在困境中.在 DB-DRSN 中使用 DB-DRSN.这是一个EEGEEGEEGEEGEEGEEGEEG.实时预警实时预警视力受损的人视力受损的人.

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