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

翻译：人体活动识别（HAR）是可穿戴与泛在计算领域的核心研究主题之一。随着基于深度学习（DL）分析方法的发展，现已能够以端到端方式提取高层特征并进行分类。尽管深度学习方法整体表现优异，但受限于典型HAR应用中可用标注样本数据量通常较小且往往不充足，基于DL的HAR可能会遭遇过拟合问题。针对这一挑战，我们提出ConvBoost——一种面向卷积网络的新型三层结构化模型架构与增强框架。该框架从三个不同视角生成额外训练数据以改进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、数据增强层与弹性层，我们开发了三种"增强器"——R-Frame、Mix-up和C-Drop，分别通过密集采样、合成和模拟来丰富逐轮训练数据。这些新提出的概念层与增强器基于传感器数据特性和帧级HAR概念设计，具有普遍适用于任意卷积网络的特性。我们在三个标准基准数据集（Opportunity、PAMAP2、GOTOV）上的实验评估表明，ConvBoost框架对基于卷积网络变体（原始CNN、ConvLSTM和注意力模型）的HAR应用均有效，且所有模型均获得了显著性能提升。这表明所提方法具有通用性，可作为提升现有ConvNet类HAR模型性能的实用解决方案。本项目为开源项目，代码详见https://github.com/sshao2013/ConvBoost。