Evaluation of an accelerometer-based monitor for detecting bed net use and human entry/exit using a machine learning algorithm.

Understanding how LLINs are used is crucial for planning malaria prevention programmes. Accelerometer-based systems provide a promising new methodology for studying LLIN use. Further work exploring accelerometer placement, frequency of measurements and other machine learning approaches could make these methods even more accurate in the future.