A visualization and analysis approach of cyclist data obtained through sensors

Solutions for smart cities are being created everywhere in the world, using technology to improve urban infrastructure and make urban centers more efficient and better to live. In this way, the proposal of this work is based on the capture of information through sensors in a smart city context. Sensors are coupled on a bicycle and connected to an Arduino and a Mobile Application. After this, the captured data are saved in a cloud database, displayed and analyzed through a Web Application. In this work, our methodology is organized in three main phases: (i) data collection of the surface in which the cyclist is traveling, and the ultrasonic distance sensor, to identify areas of risk based on the proximity of objects from bicycle, (ii) data analysis and data classification, using machine learning concepts and (iii) data visualization, using map views in a Web Application. This methodology allows the identification of injury risk situations to cyclists. The main contributions of this work are surfaces classification with data collected by the accelerometer and ultrasonic sensor generating useful information through simple data. Real experiments were conducted at Fortaleza (Ceara, Brazil) and Aracati (Ceara, Brazil). This work brings new perspectives to collaborative data collection for identification of injury risk situations to cyclists, since it can be used to suggest routes based on these risk indicators and offer a secure environment for cyclists.