Human Action Recognition plays a vital role in assistive technologies deployed in real-world scenarios, where models must address challenges related to privacy, contextual reasoning, and robustness beyond controlled benchmarks. In this work, we introduce a novel taxonomy that classifies HAR models along two primary axes: the level of privacy ensured by the input modality and the model’s readiness for deployment in unconstrained environments. This classification is further informed by two semantic dimensions -- temporal modeling and contextual awareness, which capture the model's ability to reason over time and integrate environmental cues. Based on this taxonomy, we define the Privacy-Conscious Readiness and Modality Semantics score, a composite metric prioritising privacy while rewarding semantic richness and deployment feasibility. Our contribution enables a structured and ethics-aware evaluation of HAR models, facilitating their responsible use in assistive applications.