Multimodal Data Fusion to Capture Dynamic Interactions between Built Environment and Vulnerable Older Adults

TL;DR

This paper introduces a novel multimodal data fusion method combining wearable sensors and environmental data to better understand how urban environments affect mobility and perception of vulnerable older adults, aiding inclusive city planning.

Contribution

It presents a new integrated framework for real-time human-environment interaction analysis using synchronized multimodal data streams, advancing urban design for vulnerable populations.

Findings

AI-driven data fusion identifies significant urban segments affecting mobility.

Preliminary results demonstrate insights into behavior and perception of older adults.

Framework supports evidence-based, age-friendly urban planning.

Abstract

Ensuring safe and inclusive mobility for vulnerable older adults is an emerging priority in urban planning. However, existing data sources such as surveys or GIS-based audits provide limited insight into how micro-scale built environment (BE) features influence real-world behavior and perception. This study presents a novel multimodal data-fusion approach that integrates wearable and environmental sensing to dynamically represent human-environment interactions and quantify the BE impacts on mobility among vulnerable older adults, specifically those with knee osteoarthritis or a history of falls. Data collected during naturalistic walking sessions in Singapore, are used to demonstrate this framework of synchronized streams from eye tracking, kinematic sensors, physiological monitors, GPS, and video recordings. Preliminary results show how AI-driven data fusion can uncover behaviorally and perceptually significant urban segments, providing a basis for actionable insights in inclusive design. This human-centered analytical approach advances the representation of urban environments from the perspective of vulnerable pedestrians, establishing a foundation for evidence-based, age-friendly city planning.