1 results listed
The basis of biometric authentication is that each
person's physical and behavioral characteristics can be accurately
defined. Many authentication techniques were developed for
years. Human gait recognition is one of these techniques. This
article was studied on HugaDB database which is a human gait
data collection for analysis and activity recognition (2017,
Chereshnev and Kertesz-Farkas). Combined activity data of
different people were collected in HugaDB database (2017,
Chereshnev and Kertesz-Farkas). The activities are walking,
running, sitting and standing (2017, Chereshnev and KerteszFarkas).
The data were collected with devices such as wearable
accelerometer and gyroscope (2017, Chereshnev and KerteszFarkas).
Only the walking dataset of the HugaDB was used
artificial neural network-based method for real-time gait analysis
with the minimal number of Inertial Measurement Units (2018,
Sun et al). In this paper, each person is considered as a different
class because there are multiple users' gait data in the database
and some machine learning algorithms have been applied to
walking, running, standing and sitting data. The best algorithms
are chosen from the algorithms applied to the HugaDB data and
the results are shared.
International Conference on Cyber Security and Computer Science
ICONCS
Aybüke KEÇECİ
Armağan YILDIRAK
Kaan ÖZYAZICI
Gülşen AYLUÇTARHAN
Onur AĞBULUT
İbrahim ZİNCİR