Recognizing Abnormal Activity Using MultiClass SVM Classification Approach in Tele-health Care

Abstract

Anomalies are cases of an active class that vary in their performance from the standard or planned sequence of events. Early identification of body sensor networks is an essential and difficult role and has many possible benefits. Both these tasks are very important for the monitoring of suspicious behavior to capture human activities and evaluate these results. Data collection is important if the true evidence of human behavior is to be discovered. Since it uses the technique of state table transformation to find suspicious behaviors, identify them and track them from a distant location for elderly or children. This article offers an automatic activity surveillance system that identifies common activities that usually occur in a human routine. SVM is used here E-MultiClass (Support Vector Machine). It is used to rate two data groups with a binary score. This paper describes our task of data acquisition, recording them in the field of imagery processing and validating our algorithms on data obtained from remote sensors. It is also used to detect improvements in human routine as well as everyday life across these capabilities. It describes our activities in the field of image processing.