Human Activity Recognition using time-series data from wearable sensors poses unique challenges due to complex temporal dependencies, sensor noise, placement variability, and diverse human behaviors. These factors, combined with the nontransparent nature of black-box Machine Learning models impede interpretability and hinder human comprehension of model behavior. This paper addresses these challenges by exploring strategies to enhance interpretability through white-box approaches, which provide actionable insights into latent space dynamics and model behavior during training. By leveraging human intuition and expertise, the proposed framework improves explainability, fosters trust, and promotes transparent Human Activity Recognition systems. A key contribution is the proposal of a Human-in-the-Loop framework that enables dynamic user interaction with models, facilitating iterative refinements to enhance performance and efficiency. Additionally, we investigate the usefulness of Large Language Model as an assistance to provide users with guidance for interpreting visualizations, diagnosing issues, and optimizing workflows. Together, these contributions present a scalable and efficient framework for developing interpretable and accessible Human Activity Recognition systems.

翻译：利用可穿戴传感器的时间序列数据进行人类活动识别面临独特挑战，包括复杂的时间依赖性、传感器噪声、放置位置可变性以及多样化的人类行为。这些因素与黑盒机器学习模型的不透明性相结合，阻碍了模型的可解释性，并妨碍人类对模型行为的理解。本文通过探索白盒方法增强可解释性的策略来解决这些挑战，这些方法在训练过程中提供了对潜在空间动态和模型行为的可操作见解。通过利用人类直觉和专业知识，所提出的框架提高了可解释性，培养了信任，并促进了透明的人类活动识别系统。一个关键贡献是提出了一个人在回路框架，该框架支持用户与模型的动态交互，促进迭代优化以提升性能和效率。此外，我们研究了大型语言模型作为辅助工具的有用性，为用户提供解释可视化、诊断问题和优化工作流程的指导。这些贡献共同提出了一个可扩展且高效的框架，用于开发可解释且易于访问的人类活动识别系统。