关于在基于深度学习的动作传感器的人类活动识别中使用卷积块注意力模块的使用
Sumeyye Agac1, Ozlem Durmaz Incel1
1Department of Computer Engineering, Bogazici University, Istanbul 34342, Turkey.
Diagnostics (Basel, Switzerland)
|June 10, 2023
概括
卷积块注意模块 (CBAM) 增强了基于传感器的人类活动识别的深度学习模型. 空间注意力改善了机遇数据集,而道注意力提高了Pamap2数据集,参数增加最小.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 基于传感器的人类活动识别 (HAR) 使用可穿戴设备和深度学习模型.
- 基于注意力的模型越来越多地被用于通过动态调功能来提高HAR性能.
- 卷积块注意模块 (CBAM) 对HAR的DeepConvLSTM模型的影响仍未得到充分研究.
研究的目的:
- 调查CBAM的性能,包括通道和空间注意力,在HAR的DeepConvLSTM架构上.
- 分析CBAM模块所需的额外参数,以优化可穿戴设备中的资源.
- 评估CBAM在提高基准数据集识别准确性的有效性.
主要方法:
- 将CBAM的个人和组合通道和空间注意力机制应用于DeepConvLSTM模型.
- 使用Pamap2 (12项活动) 和Opportunity (18项微型活动) 数据集评估模型性能.
- 与基线DeepConvLSTM模型相比较的识别性能和额外的参数计数.
主要成果:
- 空间注意力改善了机遇数据集的宏观F1评分,从0.74提高到0.77.
- 道关注使Pamap2数据集的性能从0.95提高到0.96,参数的增加是可以忽略的.
- 注意力机制提高了基线模型中得分较低的活动的表现.
结论:
- CBAM有效地增强了基于传感器的HAR的DeepConvLSTM,改善了基准数据集的性能.
- 这项研究表明,注意力机制有潜力优化资源有限的可穿戴设备的HAR模型.
- 结合CBAM和DeepConvLSTM方法,与使用相同数据集的相关研究相比,取得了更好的结果.
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