Forecasting Occupational Survivability of Rickshaw Pullers in a Changing Climate with Wearable Data

While the vulnerability of cycle rickshaw pullers to extreme heat is well recognized, little effort has been devoted to modeling how their physiological biomarkers respond under such conditions. In this study, we collect real-time weather and physiological data using a wearable computing platform from 100 rickshaw pullers in Dhaka, Bangladesh. In parallel, we interview 12 additional rickshaw pullers to explore their knowledge, perceptions, and experiences related to climate change. We propose a Linear Gaussian Bayesian Network (LGBN)-based regression model that predicts key physiological biomarkers based on activity, weather, and demographic features. The model achieves normalized mean absolute error (NMAE) of 0.82, 0.47, 0.65, and 0.67, respectively, for the biomarker: skin temperature, relative cardiac cost, skin conductance response, and skin conductance level. Using climate model projections from 18 CMIP6 global climate models, we layer the LGBN on top of future climate forecasts to conduct a survivability analysis for both current (2023-2025) and future years (2026-2100). Based on the criteria T WEGT > 31.1° C and T skiin > 35°C, the analysis shows that a significant percentage of rickshaw pullers (32%) are already facing a high risk of heat-related illness or prolonged exposure to extreme heat (T WBGT > 31.1°C) during regular work hours. In future years, e.g., 2026–2030, based on the CMIP6-based climate models, this percentage can rise to 37 ±17% with an exposure duration of 11.9 ±2 minutes (68% of the trip duration) on average. A similar trend is found based on rickshaw pullers' skin temperature with exposure (T skin > 35°C) durations expanding from 11 minutes (64% of the trip duration) to 13 ± 2 minutes (73% of the trip duration) by 2026-2030. Finally, a Thematic Analysis of interview data provides qualitative insights that complement the current observation and model's predictions in the future. The findings reveal that rickshaw