Human Activity Recognition (HAR) has become a spotlight in recent scientific research because of its applications in various domains such as healthcare, athletic competitions, smart cities, and smart home. While researchers focus on the methodology of processing data, users wonder if the Artificial Intelligence (AI) methods used for HAR can be trusted. Trust depends mainly on the reliability or robustness of the system. To investigate the robustness of HAR systems, we analyzed several suitable current public datasets and selected WISDM for our investigation of Deep Learning approaches. While the published specification of WISDM matched our fundamental requirements (e.g., large, balanced, multi-hardware), several hidden issues were found in the course of our analysis. These issues reduce the performance and the overall trust of the classifier. By identifying the problems and repairing the dataset, the performance of the classifier was increased. This paper presents the methods by which other researchers may identify and correct similar problems in public datasets. By fixing the issues dataset veracity is improved, which increases the overall trust in the trained HAR system.

翻译：人体活动识别（Human Activity Recognition, HAR）因其在医疗健康、体育竞赛、智慧城市及智能家居等领域的广泛应用，已成为近年科学研究的热点。当研究者聚焦于数据处理方法时，用户开始质疑用于HAR的人工智能（AI）方法是否值得信赖。信任主要取决于系统的可靠性或鲁棒性。为探究HAR系统的鲁棒性，我们分析了多个当前适用的公开数据集，并选定WISDM作为深度学习方法的探究对象。尽管WISDM的公开规格满足我们的基本要求（如规模大、类别均衡、多硬件支持），但在分析过程中仍发现若干隐藏问题。这些问题降低了分类器的性能及其整体可信度。通过识别问题并修复数据集，分类器的性能得到提升。本文阐述了研究者识别并修正公开数据集中类似问题的方法。修复后数据集的真实性得以改进，从而增强了训练所得HAR系统的整体可信度。