Machine Learning and AI Software Engineering
Niš, Central Serbia, Serbia
Human activity detection has seen immense growth in the last decade playing an important role in the field of pervasive computing. This popularity can be credited to its real-life applications primarily dealing with human-centric problems in general medicine. Many research attempts with data mining and machine learning techniques have been undergoing to accurately detect human activities for healthcare systems. This speech reviews some of the predictive machine learning algorithms and compares the accuracy and performances of these models.
Using CNNs we have managed to broadly categorize the target activities as moving (walking, walking upstairs downstairs) and sedentary (sitting, standing and lying). The output of the model in the form of contingency tables demonstrated a strong ability to separate between the activity categories and distinguished the separate activities in the categories with lower, but still notable success rates.