Estimating Driving Behavior by a Smart Phone

Smart systems aimed at detecting the fall of a person have increased significantly due to recent technological advances and availability of modular electronics. This work presents the use of em-bedded accelerometer and gyroscope in mobile phones to accurately detect and classify the type of fall a person is experiencing before suffering an impact. Early classification of fall type helps in optimizing the algorithm of the fall detection. User acceptance, feasibility and the limitations in the accuracy of the existing devices have also been considered in this study. High efficiency and low power approaches were emphasized with wireless capability that enhanced the system per-formance for variety of applications. There is a need of reducing the time for analyzing the smart algorithms designed. It is also emphasized that this application will be a good platform that can be used to test various algorithms and multiple sensors at a time with ease and obtain data analysis in a short period