HUMAN ACTIVITY MONITORING USING SMARTPHONE

Abstract: The main aim of the project is to develop an algorithm which will classify the activity performed by a human who is carrying a smart phone. The day to day life made humans very busy at work and during daily activities, mostly elderly people who are at home have an important need to monitor their activity by others when they are alone, if they are inactive for a long time without movement, or in some situations like if they have fallen down, became unconscious for sometime or seized with a cardiac arrest etc… will help the observer to know the state of activity of person being monitored. In this project we develop an algorithm to know the activity of a person using accelerometer available in Smartphone. We have extracted the Smartphone accelerometer data using an application called accelerometer data logger version 1.0 available in Smartphone market and have processed the data in Matlab for classifying the different activities of human being into static and dynamic activity, if the activity is dynamic then further classification into walking or running is performed with the algorithm. We implemented smoothening filters for data analysis and statistical techniques like standard deviation, mean and signal magnitude analysis for activity classification. This classification algorithm will let us know the type of activity either static or dynamic and then classify the position of the user, such as walking, running or ideal, which can provide useful information for the observer who is monitoring the activities of wearer, and which will help the wearer for his daily living. To bring out the extensive use of algorithm and to provide valuable feedback for wearer regarding his activities, energy spent by user during the activities was calculated at a given time using regression methods and was implemented in the algorithm. The developed model was able to estimate the energy spent by the user, the observations recorded were almost similar to the treadmill data which is taken as a standard for our model and the mean error is not more than ±2 for 30 observations. The final results when compared with the standard model was proved to be 93 % accurate on average of 30 subjects data which is used for verifying the algorithm developed. With these set of results we have come to a conclusion that algorithm can be easily implemented in a real time Smartphone application with low false predictions and can be implemented with low computational cost and fast real-time response. In future our classification algorithm can also be used in military applications where one can know what the soldier is doing without actually seeing him and additionally it can be proved as a support system in athlete’s health monitoring and training.