Background: As people age, navigating unfamiliar urban environments becomes increasingly challenging. Declines in spatial cognition, memory, and physical mobility interact and compound one another [1][3], while fear of disorientation generates additional anxiety. Together, these pressures can discourage older adults from venturing out, contributing to loneliness and social isolation [2]. What the Body Reveals: Wearable devices allow us to measure physiological responses that shift with an individual's circumstances. Indicators such as electrodermal activity (EDA), photoplethysmography (PPG), heart rate variability (HRV), and changes in gait and stride variability signal when an older adult is experiencing stress during navigation [2]. Environmental factors including complexity, crowds, noise, and hazards all contribute to that response. From Signal to System: Machine learning pipelines combine EDA, PPG, HRV [2] and GPS data can detect stress responses with high accuracy, mapping them as risk hotspots across urban space. AR navigation aids and adaptive routing systems show real promise, yet most current tools do not respond to real-time physiological state. Adaptive Navigation: This poster presents ongoing research where wearable sensors detect navigational stress and routing algorithms respond with notifications offering alternative paths, rest points or wayfinding assurance. The guidance adapts in real time to the individual’s stress levels. Realising this at scale requires interdisciplinary collaboration across gerontechnology, urban design, and human-computer interaction. References 1. Fricke et al. (2022). Frontiers in Psychology, 13, 867987. 2. Pataca et al. (2025). Computers in Biology and Medicine, 198, 111166. 3. van der Ham & Claessen (2020). Ageing Research Reviews, 58, 101020.