A pragmatic signal processing approach for nurse care activity recognition using classical machine learning

Nursing activity recognition adds a new dimension to the healthcare automation system. But nursing activity recognition is very challenging than identifying simple human activities like walking, cycling, swimming, etc. due to intra-class variability between activities. Besides, the lack of proper dataset does not allow researchers to develop a generalized method for nursing activity or comparing baseline methods on different datasets. Nurse Care Activity Recognition Challenge 2020 provides a dataset of twelve nursing activities. In this paper, we have described our (Team Hex Code) approach where we have emphasized on developing method, which can cope up with real-world data with noise and uncertainty. In our method, we have resampled our data to deal with a variable sample frequency of dataset and we have also applied feature selection method on the extracted feature to have the best combination of feature set for classification. We have used random forest classifier which is a classical machine learning algorithm. Applying our methodology, we have got 78% validation accuracy on the dataset. We have trained our model on the lab dataset and validate them on the field dataset.