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This research aims to enhance activity recognition for elderly individuals using wrist-worn multisensors and genetic algorithms.

Genetic Algorithm-Based Classifiers Fusion for Multisensor Activity Recognition of Elderly People

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Randomized trial investigates activity recognition using multisensors in elderly individuals, suggesting improved accuracy.

This research aims to enhance activity recognition for elderly individuals using wrist-worn multisensors and genetic algorithms.
Utilized wrist-worn multisensors including accelerometers and heart rate monitors for activity recognition.
Applied a genetic algorithm-based fusion weight selection approach to optimize classifier accuracy.
Analyzed various combinations of classifiers and fusion techniques.
GAFW achieved equal or higher accuracy than the best individual classifier 98% of the time, indicating significant improvement.
The study emphasized the critical role of accelerometers in the activity recognition process.
Heart rate data was shown to improve classification accuracy across activities with varying heart rates.

Chernbumroong et al. (2014) studied this question.

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