Bayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions
Fig 3
Performance of the Bayesian structural time series model in model experiments with known interventions.
A) Using an indoor air quality sensor, CO2 is measured with a variety of other covariates. The intervention (arrival of office members) causes an increase in CO2 and is determined to be impactful using the model. B) Accelerometer measurements of a person spinning in a chair holding sensor near body and then extended to arm’s length at the intervention; C) with simulated “noise” produced by a hop during the intervention period and D) with a paired covariate (second sensor) that is not affected by the intervention but experiences the “noise” hop. Sensitivity analysis shows comparison of mhealthCI to ARIMA, with the vertical axis as the fraction of the intervention that correctly identified a non-zero impact.