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This study explores the role of User-Centered Design (UCD) in improving the usability and adoption of AI-based fall detection systems. Despite advances in artificial intelligence and wearable healthcare technology, usability challenges, including alert fatigue, complex interfaces, and poor customization, its limit system effectiveness and user adoption. This paper systematically reviews the usability challenges, UX solutions, and technology adoption models related to AI-driven fall detection using a PRISMA-based SLR methodology. Thematic analysis of selected studies identifies key usability barriers, UX enhancement strategies, and the role of TAM & UTAUT adoption models in driving system engagement. Findings suggest that multimodal UX, AI explainability, and adaptive alerts significantly improve adoption rates. The study provides design recommendations and future research directions for enhancing AI-driven fall detection through UCD methodologies.

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Bridging Usability and User Experience in AI-Based Fall Detection: A Systematic Literature Review on User-Centered Design Approach for Enhanced Adoption

  • 会议论文

  • 发表日期：

    2026年

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  • Anwar E. Khidzir,
  • Waidah Ismail,
  • Mahadi Bahari,
  • Ali Y. Aldailamy

摘要

This study explores the role of User-Centered Design (UCD) in improving the usability and adoption of AI-based fall detection systems. Despite advances in artificial intelligence and wearable healthcare technology, usability challenges, including alert fatigue, complex interfaces, and poor customization, its limit system effectiveness and user adoption. This paper systematically reviews the usability challenges, UX solutions, and technology adoption models related to AI-driven fall detection using a PRISMA-based SLR methodology. Thematic analysis of selected studies identifies key usability barriers, UX enhancement strategies, and the role of TAM & UTAUT adoption models in driving system engagement. Findings suggest that multimodal UX, AI explainability, and adaptive alerts significantly improve adoption rates. The study provides design recommendations and future research directions for enhancing AI-driven fall detection through UCD methodologies.