Abstract
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.
Highlights
Smart phone highly influences the human life from day to night
Rest of the paper is organized as follows: Section II provides details about literature review of k-nearest neighbor (K-NN) and EANN, Section III system architecture is discussed in detail; Section IV provides detail about system implementation Section V result is discussed Section VI conclusion and discussion are included
Activity recognition is done by utilizing accelerometer sensor which is embedded in smart phones to recognize the basic user activity through client/server architecture
Summary
Smart phone highly influences the human life from day to night. Many applications smarten the exertion of human like photo shoot, mailing etc. with the help of advanced technologies. Human activities detected using sensor incorporates many activities (sitting, standing, walking, running, walking upstairs, walking downstairs). This activity detecting sensors embedded even in smart phones. Abnormal activities alone detected and send to the doctor. This will be much useful to the caretaker of aged persons, physically challenged and cognitive disorders. Science & Engineering at Coimbatore Institute of Technology, India. Engineering at Coimbatore Institute of Technology, India. After predicting the test data class, if any abnormal human activity is detected message will be sent to the responsible person. Rest of the paper is organized as follows: Section II provides details about literature review of K-NN and EANN, Section III system architecture is discussed in detail; Section IV provides detail about system implementation Section V result is discussed Section VI conclusion and discussion are included
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