Tools
Tools
Bommu, Sandeep Reddy (2018) PSO trained Artificial Neural Networks Methods for Estimating Human Energy Expenditure. Masters thesis, Dublin, National College of Ireland.
Preview |
PDF (Master of Science)
Download (1MB) | Preview |
Abstract
Technology is shaping the world around us and there is a continuous debate on the impacts of technology on human health. New-age Machines have reduced human labor and adversely affected our health but the advancement in the medical science and integration of high grade sensor and wearable devices have revolutionized the knowledge about the health issues. Obesity is growing at a staggering pace and much worse are its effects in terms of diabetes, hypertension and cardiovascular disorders. The control and cure for obesity primarily lies in losing calories through various exercises. The precise measurement of the energy expenditure from the human body is therefore of critical importance in curing obesity and thereby many other implied disorders. Despite of several attempts, accurate measurement of the energy estimation is still an unsolved puzzle. Most researchers have uses different machine learning algorithms to predict the energy expenditure. The ANN combined with PSO worked well on concepts of structural engineering, efficient energy usage which instilled an idea of using this to solve the EE riddle. In this research, we utilized the public repository data of activities and ran state of the art techniques along with proposed PSO-ANN model. The results obtained were compared and discussed with evaluation metrics like RMSE and MAE.
| Item Type: | Thesis (Masters) |
|---|---|
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software R Medicine > Healthcare Industry |
| Divisions: | School of Computing > Master of Science in Data Analytics |
| Depositing User: | Caoimhe Ní Mhaicín |
| Date Deposited: | 05 Nov 2018 14:33 |
| Last Modified: | 05 Nov 2018 14:33 |
| URI: | https://norma.ncirl.ie/id/eprint/3433 |
Actions (login required)
 |
View Item |