While datasets on everyday actions, sports, and cooking are abundant, there's a significant scarcity in datasets focused on industrial domain activities, especially for distinguishing between proper and improper actions. This shortage poses a unique challenge, necessitating highly precise, context-sensitive feature extraction due to the subtle class distinctions, which are more nuanced than in general action recognition. To address this gap, we introduce a dataset featuring contrasting pairs of proper and improper actions, aimed at exploring these specific challenges, assessing the limitations of current methods, and establishing a new standard. Our dataset not only encompasses traditional industrial tasks, such as working at heights, but also extends to everyday situations like basketball, underscoring the task's broad relevance. By evaluating leading techniques on this dataset, we aim to unearth valuable insights, pushing the boundaries of action understanding in both industrial and everyday contexts.