Ethical and Practical Trade-offs in Human Action Recognition Models for Assistive Systems

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 uncon strained 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.