In the realm of human activity recognition (HAR), the integration of explainable Artificial Intelligence (XAI) emerges as a critical necessity to elucidate the decision-making processes of complex models, fostering transparency and trust. Traditional explanatory methods like Class Activation Mapping (CAM) and attention mechanisms, although effective in highlighting regions vital for decisions in various contexts, prove inadequate for HAR. This inadequacy stems from the inherently abstract nature of HAR data, rendering these explanations obscure. In contrast, state-of-th-art post-hoc interpretation techniques for time series can explain the model from other perspectives. However, this requires extra effort. It usually takes 10 to 20 seconds to generate an explanation. To overcome these challenges, we proposes a novel, model-agnostic framework that enhances both the interpretability and efficacy of HAR models through the strategic use of competitive data augmentation. This innovative approach does not rely on any particular model architecture, thereby broadening its applicability across various HAR models. By implementing competitive data augmentation, our framework provides intuitive and accessible explanations of model decisions, thereby significantly advancing the interpretability of HAR systems without compromising on performance.

翻译：在人类活动识别领域，可解释人工智能的整合已成为阐明复杂模型决策过程、促进透明度与信任的关键需求。传统解释方法（如类别激活映射和注意力机制）虽然在凸显各类情境中关键决策区域方面表现有效，但被证明不适用于人类活动识别。这种不足源于人类活动识别数据固有的抽象性，使得这些解释变得晦涩难懂。相比之下，针对时间序列的先进事后解释技术能够从其他角度解释模型，但这需要额外计算开销——通常需要10至20秒生成解释。为克服这些挑战，本研究提出一种新颖的模型无关框架，通过竞争性数据增强策略，同步提升人类活动识别模型的可解释性与效能。该创新方法不依赖特定模型架构，从而拓宽了其在各类人类活动识别模型中的适用性。通过实施竞争性数据增强，本框架能提供直观易懂的模型决策解释，在保持性能的同时显著推进人类活动识别系统的可解释性发展。