Identifying causal relationships in time-series data from a pair of wearable sensors

Abstract

According to the Lancet report on global burden of disease published in October 2020, air pollution is amongst the five highest risk factors for global health, reducing life expectancy on average by 20 months. This paper describes a data-driven method for establishing causal relationships between two time-series data streams derived from wearable sensors: personal exposure to airborne particulate matter (PM) of aerodynamic sizes less than 2.5 $\mu \mathrm{m}(\text{PM}_{2.5})$ gathered from the Airspeck monitor, and continuous respiratory rate (breaths/minute) measured by the wireless Respeck monitor worn as a plaster on the chest. Results are presented for a cohort of asthmatic adolescents using the PCMCI method on the short-term causal relationship between $\text{PM}_{2.5}$ exposure and respiratory rate for time lags in the first 60 minutes at minute-level intervals, and for time lags between 2 to 8 hours at 10-minute time intervals. For the first time a personalised exposure-response relationship between $\text{PM}_{2.5}$ exposure and respiratory rate has been demonstrated for short-term effects in asthmatic adolescents during their every day lives.