Student Work
Smartphone Gait Authentication: Recognizing Smartphone Users based on their Gait
PublicDownloadable Content
open in viewerGait recognition using smartphone motion sensors such as accelerometers and gyroscopes is relatively underdeveloped compared to those using machine vision. This project explored the various state of the art neural networks-based approaches for accelerometer and gyroscope-based gait analysis and evaluated them. CNN and LSTM neural networks architectures proposed in prior work are replicated to achieve similar results on a gait dataset gathered in the wild. Prior work focused deep learning models for gait recognition on data gathered in controlled user studies.
- This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.
- Creator
- Publisher
- Identifier
- E-project-051620-032810
- Advisor
- Year
- 2020
- Date created
- 2020-05-16
- Resource type
- Major
- Rights statement
Relations
- In Collection:
Items
Items
Thumbnail | Title | Visibility | Embargo Release Date | Actions |
---|---|---|---|---|
MQP_Report_final_-_tminhtet.pdf | Public | Download |
Permanent link to this page: https://digital.wpi.edu/show/vq27zq84p