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Evaluation of an accelerometer-based monitor for detecting bed net use and human entry/exit using a machine learning
Guibehi B Koudou1,2, April Monroe3, Seth R Irish4
1Centre Suisse de Recherches Scientifiques en Côte d'Ivoire, 01 BP 1303, Abidjan 01, Côte d'Ivoire.
Malaria Journal
|March 13, 2022
Summary
Accelerometer-based systems offer a novel method for monitoring long-lasting insecticidal bed net (LLIN) usage, improving malaria prevention programs. This technology accurately tracks various LLIN use behaviors, providing granular data beyond traditional surveys.
Area of Science:
- Malariology
- Biomedical Engineering
- Data Science
Background:
- Long-lasting insecticidal nets (LLINs) are a primary malaria control strategy.
- Understanding LLIN usage is vital for effective malaria prevention programs.
- Standard surveys have limitations in capturing detailed LLIN use patterns.
Purpose of the Study:
- To evaluate an accelerometer-based approach for measuring LLIN use behaviors.
- To establish a proof of concept for detailed, long-term LLIN use monitoring.
- To assess the performance of machine learning models in classifying LLIN activities.
Main Methods:
- Controlled study in Liverpool, UK (May-July 2018).
- An accelerometer was attached to LLINs to record five behaviors: unfurling, entering, sleeping, exiting, and folding.
- Supervised machine learning (randomForest) was used to classify 20-second accelerometer data epochs.
Main Results:
- A three-category model achieved 96.2% accuracy, distinguishing sleeping, net up, and net down/enter/exit.
- Simplified models significantly improved accuracy and sensitivity for detecting net use.
- Adults' net entry/exit detection was more accurate than children's (87.8% vs 70.0%).
Conclusions:
- Accelerometer-based systems show promise for detailed LLIN use monitoring.
- This technology can enhance malaria prevention program planning.
- Future research should optimize accelerometer placement and explore advanced machine learning techniques.
