HUMAN ACTIVITY RECOGNITION USING SMARTPHONES

Abstract

Human Activity Recognition (HAR) will become an essential part in activity recognition for general medical and health care services. In this study, we have designed and implemented an android application for the ease of caretakers which aims to send an alert message to the attendee’s contact number about the Alzheimer’s patient activity stats performed in a certain time interval. Using inbuilt sensors of smart phones we are collecting patient’s data in our application and classifying it using a supervised learning algorithm KNN. There are two actions that are selected for recognition i.e. standing and walking. We have tried our best to optimize our training model by collecting raw data in different scenarios as input and estimating a patient’s activity using data mining concepts and machine learning techniques and checking the accuracy by taking real time values to generate the best results for prediction of an identified activity. During the research we analysed the performance of four classification algorithms i.e. Naive Bayes (NB) , K Nearest Neighbour (KNN), J48 and Random forests. As prediction results for KNN was achieved best that’s why we have selected KNN as our classifier. Also we have observed the change in the accuracy rates of prediction model by changing the value for K in KNN. In this project we have intended to analyse the performance of classifier with limited training data sets and limited set of activities also keeping in mind the limited memory of phone storage. However, the accuracy of the system highly depend upon the selected features and the quality oftraining model.