Human Activity Recognition by Smartphone using Machine Learning Algorithm for Remote Monitoring

Human Activity Recognition has a lot of applications such as patient monitoring, rehabilitation and assisting disabled. When mobile sensors are hold to the subject’s body, they permit continuous monitoring of numerous signals patterns from the phone. This has appealing use in healthcare applications. In order to improve the state of global healthcare, numeroushealthcare devices have been introduced that allows doctors to perform remote monitoring and increase users motivationand awareness. Now a days smart phones become a part of our day to day life. The best way to implement the idea throughSmart phones. The smart phones contains various built-in sensing units like accelerometer, gyroscope, GPS, compass sensorand barometer. Using this a system is designed to capture the states of a user. Here the mobile sensor is used as an input deviceand estimate the human motion activity using data mining and machine learning techniques. Here we use the KNN classificationalgorithm in the activity recognition system which support the training and classification using accelerometer data only. We can predict the performance of these classifiers from a series of observations on human activities like walking, running, step up, and step down in an activity recognition system.