Application of Machine Learning Techniques in Human Activity Recognition
It addresses the problem of improving activity detection for e-health systems, but is incremental as it reviews existing methods without introducing new techniques.
This paper reviews and compares the accuracy and performance of predictive data mining algorithms for human activity recognition, focusing on applications in healthcare and elder care, but does not provide specific numerical results.
Human activity detection has seen a tremendous growth in the last decade playing a major role in the field of pervasive computing. This emerging popularity can be attributed to its myriad of real-life applications primarily dealing with human-centric problems like healthcare and elder care. Many research attempts with data mining and machine learning techniques have been undergoing to accurately detect human activities for e-health systems. This paper reviews some of the predictive data mining algorithms and compares the accuracy and performances of these models. A discussion on the future research directions is subsequently offered.