Human Activity Recognition (HAR), based on machine and deep learning algorithms is considered one of the most promising technologies to monitor professional and daily life activities for different categories of people (e.g., athletes, elderly, kids, employers) in order to provide a variety of services related, for example to well-being, empowering of technical performances, prevention of risky situation, and educational purposes. However, the analysis of the effectiveness and the efficiency of HAR methodologies suffers from the lack of a standard workflow, which might represent the baseline for the estimation of the quality of the developed pattern recognition models. This makes the comparison among different approaches a challenging task. In addition, researchers can make mistakes that, when not detected, definitely affect the achieved results. To mitigate such issues, this paper proposes an open-source automatic and highly configurable framework, named B-HAR, for the definition, standardization, and development of a baseline framework in order to evaluate and compare HAR methodologies. It implements the most popular data processing methods for data preparation and the most commonly used machine and deep learning pattern recognition models.

翻译：基于机器学习和深度学习算法的人体活动识别技术，被视为监测不同人群（如运动员、老年人、儿童、职场人士）日常与职业活动的最具前景技术之一，可提供健康监测、技术表现强化、风险情境预防及教育指导等多元化服务。然而，当前人体活动识别方法的效果与效率分析因缺乏标准化工作流程而受限——该流程本可作为评估模式识别模型质量的基准，导致不同方法间的比较充满挑战。此外，研究者可能引入未被察觉的错误，从而实质性影响实验结果。为缓解上述问题，本文提出名为B-HAR的开源自动化高可配置框架，旨在定义、标准化并构建评估与比较HAR方法的基线框架。该框架集成了最通用的数据处理方法用于数据预处理，以及最常用的机器学习与深度学习模式识别模型。