Physical Activity Identification using Supervised Machine Learning and based on Pulse Rate

Physical activity is one of the key components for elderly in order to be actively ageing. Pulse rate is a convenient physiological parameter to identify elderly’s physical activity since it increases with activity and decreases with rest. However, analysis and classification of pulse rate is often difficult due to personal variation during activity. This paper proposed a Case-Based Reasoning (CBR) approach to identify physical activity of elderly based on pulse rate. The proposed CBR approach has been compared with the two popular classification techniques, i.e. Support Vector Machine (SVM) and Neural Network (NN). The comparison has been conducted through an empirical experimental study where three experiments with 192 pulse rate measurement data are used. The experiment result shows that the proposed CBR approach outperforms the other two methods. Finally, the CBR approach identifies physical activity of elderly 84% accurately based on pulse rate.

@article{Ahmed2013,
  title     = {Physical Activity Identification using Supervised Machine Learning and based on Pulse Rate},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {4},
  number    = {7},
  year      = {2013},
  publisher = {The Science and Information Organization},
  author    = {Mobyen Uddin Ahmed and Amy Loutfi},
  doi       = {10.14569/IJACSA.2013.040730},
  url       = {https://doi.org/10.14569/IJACSA.2013.040730}
}