Predictive modeling of human activity recognition

Wearable technologies are increasingly popular amongst people who likes capturing their daily activities. Monitoring human activity using these devices and trying to predict the nature activity being performed remains as an interesting challenge. As the predictive models get more accurate, it might be possible to use them in various applications to support healthcare.

Here I present the detailed analysis and predictive modeling of the human activity recognition data set generated by Velloso et al. By using the training data set, I built several non-linear models by using decision tree, random forest, boosted tree as well as support vector machines to classify 5 types of human activities by using the features collected by human activity recognition devices. I also demonstrated stacking and blending of the combinations of these models that are trained using the predictions of the stand alone classifiers. Finally all individual and stacked classifiers were evaluated using an independent validation set. Random forest model gave the highest accuracy (99.9%) when tested on the validation set. Finally, the random forest model was used to predict 20 independent test cases, resulting in 100% accuracy.