Energy Expenditure Calculation with Physical Activity Recognition Using Genetic Algorithms
Physical health is associated with physical activity, physical activity also ensures the wellbeing of the humans, physical activity is recognized using body worn sensors, and three Inertial Measurement units (IMU) are used to capture the data from the sensors. The activity recognition chain consists of Data Acquisition, Preprocessing, segmentation, Feature extraction, and Classification. Different levels of research are carried out on each stage. In feature selection genetic algorithms are used but the paper proposing the memetic algorithms an enhanced version of the genetic algorithms with local search in the each stage of genetic algorithm. This technique shall eliminate the chances of energy loss and consequently increase efficiency of the current system.
KeywordsGenetic algorithm Human activity recognition Preprocessing Feature extraction MET
- 2.Reiss A, Stricker D (2012) Introducing a new benchmarked dataset for activity monitoring. In: Proceeding of ISWC’12, pp 108–109Google Scholar
- 4.Kozey SL, Lyden K et al (2010) Accelerometer output and MET values of common physical activities. US National Library of Medicine National Institutes of Health. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2924952/
- 6.Baldominos A, Isasi P, Saez Y (2017) Feature selection for physical activity recognition using genetic algorithms. IEE ExploreGoogle Scholar