Hybrid Neural Network for Human Activity Recognition

A real time detection of human movements is a practical solution to monitor aged people or mentally
challenged people with the permission of their family. Household person is needed to monitor the
elder and differently abled people. Instead of monitoring their activities with the help of other people,
smart phones are used as a remote to monitor their activities and simultaneously send the message
to their family members. The accelerometer sensor placed in the mobile phones. It is used to identify
the activities of the person who holds the mobile phones. The most commonly used classifier
technique is Naive Bayes classifier which has a limitation of handle with the large set of data. To
overcome this defect, the proposed system classifies the data using k-nearest neighbor (K-NN)
technique and Neuroevolution. This system recognize some representative human movements such
as walking, climbing upstairs, climbing downstairs, standing, sitting and running ,using a conventional
mobile equipped with a single tri-axial accelerometer sensor.