基于conformer的人类活动识别使用惯性测量单位
Sowmiya Seenath1, Menaka Dharmaraj1
1Noorul Islam Centre for Higher Education, Kanyakumari 629180, TamilNadu, India.
Sensors (Basel, Switzerland)
|September 9, 2023
概括
本研究介绍了一种基于Conformer的模型,用于使用惯性测量单位 (IMU) 进行人类活动识别 (HAR). 这种新的方法通过有效捕捉人类运动动态,显著提高了HAR的准确性.
科学领域:
- 计算机科学 计算机科学
- 生物医学工程 生物医学工程
- 信号处理 信号处理
背景情况:
- 人类活动识别 (HAR) 对医疗保健和健身等物联网应用至关重要.
- 惯性测量单位 (IMU) 为HAR提供准确的运动和方向数据.
- 像CNN,LSTM和GRU这样的现有模型在捕捉复杂的时间动态方面存在局限性.
研究的目的:
- 提出一种基于Conformer的新型模型,用于使用IMU增强HAR.
- 为了利用Conformer架构中的注意力机制,改进时间特征提取.
- 评估拟议模型的性能与已建立的HAR方法相比.
主要方法:
- 基于Conformer的HAR模型的开发,包括卷积层和注意力机制.
- 在多个Conformer块中利用自我注意和剩余连接.
- 在公开可用的数据集 (WISDM和USCHAD) 上进行培训和验证.
主要成果:
- 与CNN,LSTM和GRU相比,基于Conformer的模型实现了更高的准确性.
- 在WISDM上记录了98.1%和USCHAD上96%的高准确率.
- 带有残余连接的注意力机制促进了梯度流和改善了模型融合.
结论:
- 基于调整器的模型代表了基于IMU的哈尔的有希望的进步.
- 拟议的架构有效地捕捉了时间动态,从而实现了最先进的识别精度.
- 这种方法在各种领域具有实时物联网应用的潜力.
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