mlsanityData to 14 Oct 2026

Towards a Bathroom-Centered Human-Building Digital Twin Framework for Indoor Safety Analysis

arXiv 2606.23292 ↗First posted 22 Jun 2026cs.HC cs.AIcited by 0

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Abstract (arXiv)

Bathroom use is a critical safety challenge for older adults because wet surfaces, constrained layouts, limited support, and frequent posture transitions are concentrated within a small domestic space. These conditions create risks that cannot be adequately understood by considering either the bathroom environment or human motion in isolation. Existing bathroom safety studies mainly identify hazards, accessibility problems, or design modifications, whereas human-centered sensing studies often focus on activity recognition or fall detection without sufficient semantic understanding of the surrounding environment. This separation limits the interpretation of how older adults interact with fixtures, support surfaces, wet areas, and spatial constraints during daily bathroom activities. To address this gap, this study proposes a bathroom-centered human-building digital twin framework for interaction-aware indoor safety analysis with a specific emphasis on older adult bathroom safety. The framework conceptualizes bathroom risk as a coupled human-environment process and integrates semantic bathroom representation, skeleton-based human representation, spatial-semantic coupling, interaction-aware event analytics, and safety-oriented visualization. A Unity-based proof-of-concept prototype is developed to demonstrate the feasibility of the framework. Although the current work remains a prototype-oriented investigation, it establishes a methodological basis for analyzing older adults' bathroom safety through explicit body-environment relations and for advancing privacy-sensitive, interaction-aware digital twin applications in aging-in-place residential environments.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 61 references that are in the map. First posted on arXiv 22 Jun 2026.

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3 of 61 references.

References · 3

  1. 2021Revisiting Skeleton-based Action Recognitioncited by 257
  2. 2022Vision-based Human Fall Detection Systems using Deep Learning: A Reviewcited by 7
  3. 2024What is a Digital Twin Anyway? Deriving the Definition for the Built Environment from over 15,000 Scientific Publicationscited by 4

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