提出注意力净化机制，减少可穿戴设备上动作识别的冗余特征。
Redundant feature screening method for human activity recognition based on attention purification mechanism
- 用跨尺度注意力筛选和连接方法消除多尺度特征叠加带来的冗余。
- 在四个公开数据集上实现高准确率，资源消耗极低。
- 适合对功耗敏感的嵌入式可穿戴设备部署，实用性强。
基于传感器的人体动作识别（HAR）中，深度神经网络提供了先进技术支撑。许多研究证实，通过增加网络深度或宽度可提升识别精度。然而，对于可穿戴设备而言，网络性能与资源消耗之间的平衡至关重要。本文以最小资源消耗为原则，提出一种通用的注意力特征净化机制（MSAP），适用于多尺度网络。该机制通过跨尺度注意力筛选与连接方式，有效解决多尺度特征叠加引起的特征冗余问题。此外，设计了无缝集成于各网络模块层间的网络修正模块，缓解深层网络固有缺陷。构建了符合当前可穿戴技术水平的嵌入式部署系统，验证了HAR模型的实际可行性，进一步证明了方法的高效性。在四个公开数据集上的大量实验表明，所提方法能有效降低过滤数据中的冗余特征，且在资源消耗极少的情况下表现优异。
原文摘要 · Abstract (English)
In the field of sensor-based Human Activity Recognition (HAR), deep neural networks provide advanced technical support. Many studies have proven that recognition accuracy can be improved by increasing the depth or width of the network. However, for wearable devices, the balance between network performance and resource consumption is crucial. With minimum resource consumption as the basic principle, we propose a universal attention feature purification mechanism, called MSAP, which is suitable for multi-scale networks. The mechanism effectively solves the feature redundancy caused by the superposition of multi-scale features by means of inter-scale attention screening and connection method. In addition, we have designed a network correction module that integrates seamlessly between layers of individual network modules to mitigate inherent problems in deep networks. We also built an embedded deployment system that is in line with the current level of wearable technology to test the practical feasibility of the HAR model, and further prove the efficiency of the method. Extensive experiments on four public datasets show that the proposed method model effectively reduces redundant features in filtered data and provides excellent performance with little resource consumption.
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