In recent years, deep learning has emerged as a potent tool across a
multitude of domains, leading to a surge in research pertaining to its
application in the wearable human activity recognition (WHAR) domain. Despite
the rapid development, concerns have been raised about the lack of
standardization and consistency in the procedures used for experimental model
training, which may affect the reproducibility and reliability of research
results. In this paper, we provide an exhaustive review of contemporary deep
learning research in the field of WHAR and collate information pertaining to
the training procedure employed in various studies. Our findings suggest that a
major trend is the lack of detail provided by model training protocols.
Besides, to gain a clearer understanding of the impact of missing descriptions,
we utilize a control variables approach to assess the impact of key tunable
components (e.g., optimization techniques and early stopping criteria) on the
inter-subject generalization capabilities of HAR models. With insights from the
analyses, we define a novel integrated training procedure tailored to the WHAR
model. Empirical results derived using five well-known \ac{whar} benchmark
datasets and three classical HAR model architectures demonstrate the
effectiveness of our proposed methodology: in particular, there is a
significant improvement in macro F1 leave one subject out cross-validation
performance.

近年来，深度学习在可穿戴人体活动识别领域的应用引起了广泛关注，但对实验模型训练的标准化和一致性的关注不断增加，为了解决这个问题，本文对 WHAR 领域的深度学习研究进行了全面的回顾，并汇总了各项研究中使用的训练程序信息，结果表明现阶段存在训练过程细节不足的问题，随后将影响分析融入到控制变量方法中，评估了关键可调整组件对 HAR 模型的跨主体泛化能力的影响，基于分析结果，我们提出了一种适用于 WHAR 模型的整合训练流程，通过使用五个知名的 WHAR 基准数据集和三种经典 HAR 模型架构进行实证结果验证，证明了我们所提方法的有效性，尤其是在宏观 F1 留一主体交叉验证性能上有显著改进。

标准化人体动作识别模型的训练过程：可调参数的综合评述

Standardizing Your Training Process for Human Activity Recognition  Models: A Comprehensive Review in the Tunable Factors

Machine learning-based wearable human activity recognition (WHAR) models
enable the development of various smart and connected community applications
such as sleep pattern monitoring, medication reminders, cognitive health
assessment, sports analytics, etc. However, the widespread adoption of these
WHAR models is impeded by their degraded performance in the presence of data
distribution heterogeneities caused by the sensor placement at different body
positions, inherent biases and heterogeneities across devices, and personal and
environmental diversities. Various traditional machine learning algorithms and
transfer learning techniques have been proposed in the literature to address
the underpinning challenges of handling such data heterogeneities. Domain
adaptation is one such transfer learning techniques that has gained significant
popularity in recent literature. In this paper, we survey the recent progress
of domain adaptation techniques in the Inertial Measurement Unit (IMU)-based
human activity recognition area, discuss potential future directions.

本文综述了近期就惯性测量单位（IMU）为基础的人体活动识别领域中应用于领域自适应技术的最新研究进展，并讨论了潜在的未来方向。

基于惯性测量单元的人体活动识别领域自适应：一项调查

Domain Adaptation for Inertial Measurement Unit-based Human Activity  Recognition: A Survey

In practice, Wearable Human Activity Recognition (WHAR) models usually face
performance degradation on the new user due to user variance. Unsupervised
domain adaptation (UDA) becomes the natural solution to cross-user WHAR under
annotation scarcity. Existing UDA models usually align samples across domains
without differentiation, which ignores the difference among samples. In this
paper, we propose an unsupervised domain adaptation model with sample weight
learning (SWL-Adapt) for cross-user WHAR. SWL-Adapt calculates sample weights
according to the classification loss and domain discrimination loss of each
sample with a parameterized network. We introduce the meta-optimization based
update rule to learn this network end-to-end, which is guided by
meta-classification loss on the selected pseudo-labeled target samples.
Therefore, this network can fit a weighting function according to the
cross-user WHAR task at hand, which is superior to existing sample
differentiation rules fixed for special scenarios. Extensive experiments on
three public WHAR datasets demonstrate that SWL-Adapt achieves the
state-of-the-art performance on the cross-user WHAR task, outperforming the
best baseline by an average of 3.1% and 5.3% in accuracy and macro F1 score,
respectively.

本论文提出了一种新的基于样本权重学习的无监督领域自适应方法，称为 SWL-Adapt，用于解决新用户的穿戴式人体活动识别性能下降的问题，并在三个公共数据集上进行了广泛的实验，结果显示 SWL-Adapt 在跨用户的 WHAR 任务中表现优异，平均准确度和宏 F1 分数分别达到了 3.1％和 5.3％的最佳基线以上。