Forecasting Occupational Survivability of Rickshaw Pullers in a Changing Climate with Wearable Data
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arXiv
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| Natura: | Preprint |
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2025
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| author | Rahaman, Masfiqur Hasana, Maoyejatun Rahman, Shahad Shahriar Noor, MD Sajid Mostafiz Abedin, Razin Reaz Tahmid, Md Toki Parris, Duncan Watson Choudhury, Tanzeem Islam, A. B. M. Alim Al Rahman, Tauhidur |
| author_facet | Rahaman, Masfiqur Hasana, Maoyejatun Rahman, Shahad Shahriar Noor, MD Sajid Mostafiz Abedin, Razin Reaz Tahmid, Md Toki Parris, Duncan Watson Choudhury, Tanzeem Islam, A. B. M. Alim Al Rahman, Tauhidur |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_00081 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Forecasting Occupational Survivability of Rickshaw Pullers in a Changing Climate with Wearable Data Rahaman, Masfiqur Hasana, Maoyejatun Rahman, Shahad Shahriar Noor, MD Sajid Mostafiz Abedin, Razin Reaz Tahmid, Md Toki Parris, Duncan Watson Choudhury, Tanzeem Islam, A. B. M. Alim Al Rahman, Tauhidur Computers and Society Human-Computer Interaction Information Retrieval Machine Learning Applications 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. |
| title | Forecasting Occupational Survivability of Rickshaw Pullers in a Changing Climate with Wearable Data |
| topic | Computers and Society Human-Computer Interaction Information Retrieval Machine Learning Applications |
| url | https://arxiv.org/abs/2511.00081 |