通过基于物联网的多模式深度学习框架智能ADL识别
Madiha Javeed1, Naif Al Mudawi2, Abdulwahab Alazeb2
1Department of Computer Science, Air University, E-9, Islamabad 44000, Pakistan.
Sensors (Basel, Switzerland)
|September 28, 2023
概括
这项研究引入了使用物联网 (IoT) 设备的智能家居监控系统,以远程跟踪老年人的日常生活活动 (ADL). 多式联络方法在识别ADL时实现了84.14%的准确性.
科学领域:
- 计算机科学 计算机科学
- 老年学是一门学科.
- 生物医学工程 生物医学工程
背景情况:
- 远程监控系统对于老年护理至关重要,为家庭和护理人员提供灵活性.
- 日常生活活动 (ADLs) 为监测老年人和患者提供了有效的指标.
- 现有的系统通常依赖于单一类型的传感器,限制了全面的监控.
研究的目的:
- 通过多感应物联网 (IoT) 设备为远程老年人监控提供强大的分层架构.
- 开发一种多式联络方法,整合可穿戴传感器和视频数据,以加强ADL识别.
- 实现对老年人的日常活动进行准确可靠的远程监控.
主要方法:
- 一个分层的架构处理来自多式联网物联网传感器 (可穿戴惯性传感器,视频) 的数据.
- 预处理步骤包括数据过,细分,地标检测和2D棒模型创建.
- 进行特征提取,融合和优化,然后使用卷积神经网络 (CNN) 进行分类.
主要成果:
- 拟议的多式联运系统有效地融合了来自不同传感器的数据.
- 层次架构成功处理和分析了用于ADL识别的传感器数据.
- 在识别日常生活活动方面,获得了84.14%的可接受平均准确率.
结论:
- 开发的基于物联网的多模式分层系统证明了远程监控老年人的可行解决方案.
- 多感官数据和深度学习的整合提高了ADL识别的准确性.
- 这种方法为改善老年人在家中的安全和福祉提供了一个有希望的方向.
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