In this paper, we present an approach for human body fall detection that can be supported with modern smartphone equipped with accelerator sensors. Falling is one of the most significant causes of injury, mainly for elderly citizens, and one of the reasons why many individuals are forced to leave the comfort and privacy of their homes and live in an assisted-care environment. The accelerations provided by the sensor, corresponding to the user body motion and much of the effort has been put towards developing a robust algorithm to accurately detect a fall. Its embedded tri-accelerometer sensor was utilized to collect the information about the body motion. This data is incorporated by a real-time Pose Body Model (PBM) which is identified by an Extended Kalman Filter (EKF) algorithm. Moreover, a Support Vector Machine (SVM) performs a binary classification of the observed data, allowing the detection of fall incidents. This fall detection system is tailored for mobile phones and has an important application in the field of safety and security, but also can be used in motion analysis of body moving and live style monitoring. Experimental results shown that this methodology can detect the most of the possible types of single human falls quite accurately.