  Complex human activity recognition (CHAR) remains a pivotal challenge within
ubiquitous computing, especially in the context of smart environments. Existing
studies typically require meticulous labeling of both atomic and complex
activities, a task that is labor-intensive and prone to errors due to the
scarcity and inaccuracies of available datasets. Most prior research has
focused on datasets that either precisely label atomic activities or, at
minimum, their sequence approaches that are often impractical in real world
settings.In response, we introduce VCHAR (Variance-Driven Complex Human
Activity Recognition), a novel framework that treats the outputs of atomic
activities as a distribution over specified intervals. Leveraging generative
methodologies, VCHAR elucidates the reasoning behind complex activity
classifications through video-based explanations, accessible to users without
prior machine learning expertise. Our evaluation across three publicly
available datasets demonstrates that VCHAR enhances the accuracy of complex
activity recognition without necessitating precise temporal or sequential
labeling of atomic activities. Furthermore, user studies confirm that VCHAR's
explanations are more intelligible compared to existing methods, facilitating a
broader understanding of complex activity recognition among non-experts.
