ConvBoost：提升基于传感器的活动识别的ConvNets
ConvBoost: Boosting ConvNets for Sensor-based Activity Recognition
AI总结：
针对基于深度学习的人类活动识别中因标注数据不足导致的过拟合问题，提出ConvBoost三层架构和提升框架，通过密集采样、合成和模拟生成额外训练数据，在多个基准上显著提升了多种卷积网络的识别性能。
摘要：
人类活动识别（HAR）是普适和可穿戴计算的核心研究主题之一。随着向基于深度学习（DL）的分析方法转变，已经能够以端到端的方式提取高级特征并进行分类。尽管DL具有出色的整体能力，但典型的HAR应用中可用的标注样本数据量通常少得可怜且往往不足，这导致基于DL的HAR可能会遭受过拟合问题。为了应对这些挑战，我们提出了ConvBoost——一种新颖的三层结构化模型架构和提升框架，用于基于卷积网络的HAR。我们的框架从三个不同的角度生成额外的训练数据以改进HAR，旨在缓解该领域标注训练数据不足的问题。具体而言，通过引入三个概念层——采样层、数据增强层和弹性层——我们开发了三个“提升器”：R-Frame、Mix-up和C-Drop，分别通过密集采样、合成和模拟来丰富每个轮次的训练数据。这些新的概念层和提升器普遍适用于任何类型的卷积网络，是基于传感器数据的特征和逐帧HAR的概念设计的。在三个标准基准（Opportunity、PAMAP2、GOTOV）上的实验评估中，我们展示了ConvBoost框架在基于卷积网络变体的HAR应用中的有效性：vanilla CNN、ConvLSTM和Attention Models。我们在所有模型上都取得了显著的性能提升，这表明所提出的方法是通用的，可以作为提升现有基于ConvNet的HAR模型性能的实用解决方案。这是一个开源项目，代码可在https://github.com/sshao2013/ConvBoost找到。
英文摘要：
Human activity recognition (HAR) is one of the core research themes in ubiquitous and wearable computing. With the shift to deep learning (DL) based analysis approaches, it has become possible to extract high-level features and perform classification in an end-to-end manner. Despite their promising overall capabilities, DL-based HAR may suffer from overfitting due to the notoriously small, often inadequate, amounts of labeled sample data that are available for typical HAR applications. In response to such challenges, we propose ConvBoost -- a novel, three-layer, structured model architecture and boosting framework for convolutional network based HAR. Our framework generates additional training data from three different perspectives for improved HAR, aiming to alleviate the shortness of labeled training data in the field. Specifically, with the introduction of three conceptual layers--Sampling Layer, Data Augmentation Layer, and Resilient Layer -- we develop three "boosters" -- R-Frame, Mix-up, and C-Drop -- to enrich the per-epoch training data by dense-sampling, synthesizing, and simulating, respectively. These new conceptual layers and boosters, that are universally applicable for any kind of convolutional network, have been designed based on the characteristics of the sensor data and the concept of frame-wise HAR. In our experimental evaluation on three standard benchmarks (Opportunity, PAMAP2, GOTOV) we demonstrate the effectiveness of our ConvBoost framework for HAR applications based on variants of convolutional networks: vanilla CNN, ConvLSTM, and Attention Models. We achieved substantial performance gains for all of them, which suggests that the proposed approach is generic and can serve as a practical solution for boosting the performance of existing ConvNet-based HAR models. This is an open-source project, and the code can be found at https://github.com/sshao2013/ConvBoost
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
