Activity recognition systems that are capable of estimating human activities from wearable inertial sensors have come a long way in the past decades. Not only have state-of-the-art methods moved away from feature engineering and have fully adopted end-to-end deep learning approaches, best practices for setting up experiments, preparing datasets, and validating activity recognition approaches have similarly evolved. This tutorial was first held at the 2021 ACM International Symposium on Wearable Computers (ISWC'21) and International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp'21). The tutorial, after a short introduction in the research field of activity recognition, provides a hands-on and interactive walk-through of the most important steps in the data pipeline for the deep learning of human activities. All presentation slides shown during the tutorial, which also contain links to all code exercises, as well as the link of the GitHub page of the tutorial can be found on: https://mariusbock.github.io/dl-for-har

翻译：在过去几十年中,能够从可磨损惯性传感器中估计人类活动的活动识别系统取得了长足的进展,不仅最先进的方法脱离了特征工程,而且完全采用了端至端深学习方法、建立实验、编制数据集和验证活动识别方法的最佳做法,同样也发生了演变,在2021年ACM可磨损计算机国际专题讨论会(ISWC'21)和渗透和紫外计算国际联合会议(UbiComp'21)上首次举行了这一辅导,在活动识别研究领域作了简短介绍之后,指导性地为深入学习人类活动提供了数据管道中最重要的步骤的亲手和互动的穿行。在辅导期间展示的所有演示幻灯片也包含所有代码练习的链接,以及辅导网页GitHub的链接:https://mariusbock.github.io/dl-for-har,可在https://mariusbock.github.io/dl-for-har上找到。