University of Twente Student Theses
Cyclist weight inference from bicycle-mounted sensor data
Benitah, R.J. (2023) Cyclist weight inference from bicycle-mounted sensor data.
|
PDF
7MB |
| Abstract: | As crowd-sensing infrastructure becomes increasingly widespread, researchers are developing technologies such as bicycles with sensors for providing information about road quality. These technologies can accomplish this by processing data collected by the sensors attached to the bicycles. However, while location sensors and cameras are widely considered as privacy-sensitive data sources, seemingly innocuous sensors like accelerometers might also leak sensitive information such as the cyclist's weight. This research aims to investigate if sensitive data, such as the cyclist's weight, can be extracted from these seemingly innocuous sensors. First, it will consider the positioning of the sensing hardware devices on the bicycle. Next, users with varying weights will test the bicycle under controlled conditions. Finally, it will analyze the data and implement machine learning solutions to determine if the user's weight can be inferred. This research is expected to contribute to the knowledge of privacy considerations in the field of opportunistic and pervasive sensing. This especially true as crowd-sensing infrastructure becomes increasingly widespread and technologies such as these are developed. |
| Item Type: | Essay (Bachelor) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science, 55 traffic technology, transport technology |
| Programme: | Computer Science BSc (56964) |
| Link to this item: | https://purl.utwente.nl/essays/94344 |
| Export this item as: | BibTeX EndNote HTML Citation Reference Manager |
Show download statistics for this publication
Show download statistics for this publicationRepository Staff Only: item control page