This weekend I took part in the TreeHacks 2020 at Stanford University.
Together with my team members Marius Gutzeit, Simon Schäfer, and Benjamin Rausch we worked on an iOS smartphone application “TailGait” that allows users to authenticate themselves using unique features of their gait.
Our work was motivated by recent research results showing that gait characteristics are sufficiently distinct between humans to serve as a form of identification, similar to fingerprints or iris scans[1-3].
We started off with the simple MotionRecorder app which accesses the phone’s accelerometer and gyro at a high sampling frequency and exports the recorded data in a csv file. On top of that, our custom interface makes data recording and the authentication step easily accessible to any smartphone user.
The recorded data is sent to a server backend running python flask in the cloud, where the gait analysis is performed. We found that a simple Fourier Transform on the (gravity compensated) acceleration magnitude is already sufficient to distinguish the members of our team. The recorded data and its frequency content (Fourier Transform coefficients) can be visualized for inspection. Additinally, we also investigated the use of hand-engineered features as proposed by Steven Wessel’s post but did not use their approach in practice. Fragments of our hacky code is available on Github.
- Damaševičius, R.; Maskeliūnas, R.; Venčkauskas, A.; Woźniak, M. Smartphone User Identity Verification Using Gait Characteristics. Symmetry 2016, 8, 100. Link
- H. M. Thang, V. Q. Viet, N. Dinh Thuc and D. Choi, “Gait identification using accelerometer on mobile phone,” 2012 International Conference on Control, Automation and Information Sciences (ICCAIS), Ho Chi Minh City, 2012, pp. 344-348. Link
- Boyle, Matthew, Avraham Klausner, David Starobinski, Ari Trachtenberg, and Hongchang Wu. “Gait-based user classification using phone sensors.” In Proceedings of Conference on Mobile Systems, Applications and Services. 2011.Link