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

TL;DR

The paper addresses heat-related survivability of rickshaw pullers in Dhaka by collecting real-time wearable and environmental data from 100 workers and interviewing 12 to capture perceptions of climate change. It introduces a Linear Gaussian Bayesian Network to predict key physiological biomarkers from activity, demographics, and weather, and validates this against multiple baselines, selecting the LGBN for climate-forecast integration. By coupling 18 CMIP6 climate models with SSP scenarios, the study forecasts future biomarker trajectories, revealing rising exposure to high and extreme heat especially for $T_{WBGT}$ and $T_{skin}$ thresholds, and demonstrates that skin temperature is the most robust, climate-responsive biomarker for survivability analysis. The mixed-methods approach shows concordance between sensor-derived stress signals and workers’ reported experiences, offering a data-driven basis for targeted adaptation and policy interventions to safeguard outdoor labor under changing climates.

Abstract

Cycle rickshaw pullers are highly vulnerable to extreme heat, yet little is known about how their physiological biomarkers respond under such conditions. This study collected real-time weather and physiological data using wearable sensors from 100 rickshaw pullers in Dhaka, Bangladesh. In addition, interviews with 12 pullers explored their knowledge, perceptions, and experiences related to climate change. We developed a Linear Gaussian Bayesian Network (LGBN) regression model to predict key physiological biomarkers based on activity, weather, and demographic features. The model achieved normalized mean absolute error values of 0.82, 0.47, 0.65, and 0.67 for skin temperature, relative cardiac cost, skin conductance response, and skin conductance level, respectively. Using projections from 18 CMIP6 climate models, we layered the LGBN on future climate forecasts to analyze survivability for current (2023-2025) and future years (2026-2100). Based on thresholds of WBGT above 31.1°C and skin temperature above 35°C, 32% of rickshaw pullers already face high heat exposure risk. By 2026-2030, this percentage may rise to 37% with average exposure lasting nearly 12 minutes, or about two-thirds of the trip duration. A thematic analysis of interviews complements these findings, showing that rickshaw pullers recognize their increasing climate vulnerability and express concern about its effects on health and occupational survivability.

Figures (7)

• Figure 1: Data collection protocol with rickshaw pullers in Dhaka, showing the rickshaw, experimental timeline, and sensing devices (Embrace Plus wristband, temperature–humidity logger, and GPS smartphone).
• Figure 2: Our proposed methodology outlining each stage and subsequent tasks accomplished.
• Figure 3: Activity, Environmental and Physiological variables in three seasons and different stages of data collection. Small circles denote averages of the variables for each subject.
• Figure 4: Modeling physiological biomarkers based on weather, season, and activity as well as integration of climate model output into the model.
• Figure 5: Correlation network based on statistically significant Pearson correlation (Coefficient >= 0.1 and P $<$ 0.05) between two variables. Blue-colored and red-colored edges represent positive and negative correlations, respectively.