Enhancing Human-Computer Interaction Context Awareness through the Relativity Turner Training Algorithm

• Asma Aledrees,
• Waseem Ahmad,
• Ashit Kumar Dutta,
• Mohd Anjum,
• Sana Shahab,

Vol. 19, No. 10, October 31, 2025
10.3837/tiis.2025.10.003, Download Paper (Free):

• Context detection
• Human-Computer Interaction
• Relativity measure
• Requirement assessment
• Usability engineering

Abstract

Human-Computer Interaction for autonomous communication systems is traditionally developed through the stages of the usability engineering paradigm. However, a major challenge within this paradigm is synchronizing evolving user needs with the dynamic interaction environment. To address this gap, this article proposes the Relativity Turner Training Algorithm (RTTA). The algorithm estimates the relativity measure between the user’s interaction context and the actual requirements, allowing the system to adapt more effectively over time. Unlike rule-based techniques, the RTTA leverages machine learning to identify contextual adjustment trends and optimize requirement matching, drawing on past interaction data. The relativity factor is derived from users’ previous interaction responses. When relativity is low, the algorithm anticipates potential context changes. The “Turner” component then evaluates whether these changes can yield higher relativity—that is, a closer alignment between the new context and the user’s requirements. If such alignment is achieved, interaction quality improves, and the need for further adjustments decreases. In this way, the algorithm dynamically balances relativity (measuring context–requirement alignment) and Turner (guiding beneficial context shifts) across both short- and long-term interaction intervals. To advance research in adaptive HCI systems, the proposed RTTA framework provides a robust foundation by enabling the seamless integration of advanced machine learning techniques with multimodal interaction methods.

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