Human Activity Recognition using Reduced Kernel Extreme Learning Machine for Body Weight Management

The problem with bodyweight management is the inability to calculate the number of calories burned and consumed. Many applications can help to calculate it and one of them is implementing Human Activity Recognition using wearable sensors and smartphones. In this paper, an activity recognition model is built using the Reduced Kernel Extreme Learning Machine (RKELM) algorithm using an accelerometer sensor embedded in a smartphone that is used for calculating calories burned. This model was improved from the Extreme Learning Machine with the addition of the Gaussian kernel. The dataset comes from the London py data event in 2016 which consists of five activity labels. The proposed model will be compared with five other models and evaluated using precision, recall, f1score, training time, and testing time. The results have been validated with 10-fold cross-validation. The experimental results show that the RKELM-based recognition model has a higher performance than the other models with acceptable training and testing time, with an f1 score of 97% and less than 0.06 seconds.