SHARP:区块链驱动的WSN可实时监测学生的健康状况和个性化学习
Zeqiang Xie1, Zijian Li1, Xinbing Liu2
1College of Artificial Intelligence, Dalian Maritime University, Dalian 116026, China.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
这项研究介绍了智能教育的新型框架SHARP, 结合人工智能和区块链实时监测学生健康和个性化学习, 显著改善学习成果和数据安全.
科学领域:
- 教育技术
- 人工智能
- 区块链技术
- 物联网
- 无线传感器网络
背景情况:
- 目前的智能学习系统缺乏综合健康监测和个性化适应.
- 现有的方法存在不良生理状态预测和数据安全问题.
- 需要一个统一的实时学生福利和适应性教育框架.
研究的目的:
- 推出区块链增强的无线传感器网络 (WSN) 框架.
- 在智能教育环境中实现实时学生健康监测和个性化学习.
- 解决数据隐私,安全和系统集成方面的挑战.
主要方法:
- 使用可穿戴传感器持续收集生理数据 (心率变化,体温,压力).
- 使用深度神经网络 (DNN) 来检测身体和情绪状态.
- 实施了强化学习 (RL) 算法,用于动态的教育建议.
- 利用权威证明 (PoA) 区块链进行安全和透明的数据管理.
主要成果:
- 通过使用DNN获得94.2%的F1成绩.
- 通过RL模块减少了关键事件响应延迟.
- 与传统系统相比提高了23.5%的能源效率.
- 与对照组相比,干预组的测试成绩有156%的改善.
结论:
- SHARP 独特地集成了多传感器监控,人工智能个性化和区块链安全性.
- 与现有的物联网平台相比,它具有卓越的准确性,能源效率和数据完整性.
- 通过智能传感和自适应分析为下一代智能教育提供可扩展,保护隐私的解决方案.
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