Automatic human activity detection, mainly transport-wise, is very relevant for architects and urban planners (among many others) when designing cities, roads, public transportation systems, etc. Such detection allows to better plan our cities and has been made possible with the widespread use of smartphones carrying several different sensors.
We developed edgeTrans, a system based on a smartphone application (app, for short), a database, and a server. The database stores the trips that were done, the server runs a machine learning algorithm that generates a model (i.e., a classifier) which is then integrated into the edgeTrans app. This app, after being installed, when running in a smartphone indicates the transport mode that is being used without requiring a network connection; it can now be downloaded from the Android Play Store or from the iOS iTunes (the app is called Woorti).
The results obtained in a real-world setting are very encouraging taking into account the requirements (e.g., accuracy, and low cost).
This item's license is: Attribution 4.0 International