Human Activity Recognition using Smartphones
Mayur Sonawane Sahil Rajesh Dhayalkar Siddesh Waje Soyal Markhelkar Akshay Wattamwar Seema C. Shrawne
Human Activity Recognition is a subject of great research today and has its
applications in remote healthcare, activity tracking of the elderly or the
disables, calories burnt tracking etc. In our project, we have created an
Android application that recognizes the daily human activities and calculate
the calories burnt in real time. We first captured labeled triaxial
acceleration readings for different daily human activities from the
smartphone's embedded accelerometer. These readings were preprocessed using a
median filter. 42 features were extracted using various methods. We then tested
various machine learning algorithms along with dimensionality reduction.
Finally, in our Android application, we used the machine learning algorithm and
a subset of features that provided maximum accuracy and minimum model building
time. This is used for real-time activity recognition and calculation of
calories burnt using a formula based on Metabolic Equivalent.