| Autor: |
Daniele Di Mitri, Kevin Trebing, Stefano Bromuri, Khaleel Asyraaf Mat Sanusi |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ISBN: 9783030760625 |
| DOI: |
10.1007/978-3-030-76063-2_31 |
| Popis: |
In this paper we introduce MOBIUS, a smartphone-based system for remote tracking of citizens’ movements. By collecting smartphone’s sensor data such as accelerometer and gyroscope, along with self-report data, the MOBIUS system allows to classify the users’ mode of transportation. With the MOBIUS app the users can also activate GPS tracking to visualise their journeys and travelling speed on a map. The MOBIUS app is an example of a tracing app which can provide more insights into how people move around in an urban area. In this paper, we introduce the motivation, the architectural design and development of the MOBIUS app. To further test its validity, we run a user study collecting data from multiple users. The collected data are used to train a deep convolutional neural network architecture which classifies the transportation modes using with a mean accuracy of 89%. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
