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.

翻译：深度学习领域的一个持续趋势是将机器学习概念应用于最初引入领域之外的领域。如今，基于可穿戴传感器的先进活动识别依赖于在固定窗口数据上训练的分类器。相比之下，基于视频的人体活动识别采用了基于片段的分段预测方法，从开始到结束定位活动发生。本文首次系统性地证明了最先进的时序动作定位（TAL）模型在使用原始惯性数据作为输入的可穿戴人体活动识别（HAR）中的适用性。我们的结果表明，在6个可穿戴活动识别基准数据集中的4个上，最先进的TAL模型能够优于流行的惯性模型，F1分数提升幅度高达25%。通过将TAL领域最常用的指标（即平均精度均值）引入基于惯性的人体活动识别，我们的分析表明，TAL模型能够生成更连贯的片段，并在所有数据集上实现更高的整体NULL类准确率。作为首次提供此类分析的研究，TAL领域为基于惯性的人体活动识别提供了一个有趣的新视角，其中包含尚未探索的设计选择和训练概念，这对基于惯性的人体活动识别领域可能具有重要价值。