A persistent trend in Deep Learning has been the applicability of machine
learning concepts to other areas than originally introduced for. As of today,
state-of-the-art activity recognition from wearable sensors relies on
classifiers being trained on fixed windows of data. Contrarily, video-based
Human Activity Recognition has followed a segment-based prediction approach,
localizing activity occurrences from start to end. This paper is the first to
systematically demonstrate the applicability of state-of-the-art TAL models for
wearable Human Activity Recongition (HAR) using raw inertial data as input. Our
results show that state-of-the-art TAL models are able to outperform popular
inertial models on 4 out of 6 wearable activity recognition benchmark datasets,
with improvements ranging as much as 25% in F1-score. Introducing the TAL
community's most popular metric to inertial-based HAR, namely mean Average
Precision, our analysis shows that TAL models are able to produce more coherent
segments along with an overall higher NULL-class accuracy across all datasets.
Being the first to provide such an analysis, the TAL community offers an
interesting new perspective to inertial-based HAR with yet to be explored
design choices and training concepts, which could be of significant value for
the inertial-based HAR community.

该论文首次系统地展示了最新的 TAL 模型在使用原始惯性数据作为输入进行可穿戴式人体活动识别（HAR）方面的适用性，并利用 4 个可穿戴活动识别基准数据集证明这些模型在 F1 分数上的提高幅度可达 25%。通过引入 TAL 社区的最流行度量标准 —— 均值平均精度，论文分析表明 TAL 模型能够产生更连贯的片段，并在所有数据集上具有更高的 NULL 类别准确率。作为首次提供这样的分析，TAL 社区为惯性式 HAR 提供了一个有待探索的设计选择和训练概念的有趣新视角，对惯性式 HAR 社区具有重要价值。

基于惯性的人体活动识别的时间动作定位

Temporal Action Localization for Inertial-based Human Activity  Recognition

The goal of Temporal Action Localization (TAL) is to find the categories and
temporal boundaries of actions in an untrimmed video. Most TAL methods rely
heavily on action recognition models that are sensitive to action labels rather
than temporal boundaries. More importantly, few works consider the background
frames that are similar to action frames in pixels but dissimilar in semantics,
which also leads to inaccurate temporal boundaries. To address the challenge
above, we propose a Boundary-Aware Proposal Generation (BAPG) method with
contrastive learning. Specifically, we define the above background frames as
hard negative samples. Contrastive learning with hard negative mining is
introduced to improve the discrimination of BAPG. BAPG is independent of the
existing TAL network architecture, so it can be applied plug-and-play to
mainstream TAL models. Extensive experimental results on THUMOS14 and
ActivityNet-1.3 demonstrate that BAPG can significantly improve the performance
of TAL.

通过引入对比学习，并将背景帧作为难负样本，提出了一种独立于现有 Temporal Action Localization（TAL）网络架构的边界感知候选生成方法（BAPG），用于改善 TAL 的性能。在 THUMOS14 和 ActivityNet-1.3 数据集上的实验结果表明，BAPG 能够显著提升 TAL 的性能。