Activity recognition system using inbuilt sensors of smart mobile phone and minimizing feature vectors

Mobile phone is becoming a very popular tool due to having various user friendly applications with all flexible options. It is highly popular for its light weight, wearable and comfortable uses. Many extrinsic habitat of human being can be monitored by the help of inbuilt sensors and its application software. This has appealing use for healthcare applications using exploitation of Ambient Intelligence for daily activity monitoring system. Here, a standard dataset of UCI HAR (University of California, Irvine, Human Activity Recognition, http://archive.ics.uci.edu) is used for analysis purpose. Naive Bayes Classifier is used for recognition of runtime activities minimizing dimension of large feature vectors. Threshold based condition box is designed by us and finally these two results are compared with that of another classifier HF-SVM (Hardware Friendly-Support Vector Machine) of previous related work.