Source:

Dr. Abdulkadir Gumuscu, agumuscu '@' harran.edu.tr, Harran University Electrical and Electronics Engineering Department, Sanliurfa, Turkey

Data Set Information:

This data set was created by calculating the walking parameters of a total of 16 different volunteers, 7 female and 9 male. The volunteers of 16 volunteers ranged between 20 and 34 years old, and their weight ranged from 53 to 95. In order to calculate each walking parameter, people were asked to walk the 30-meter long course for three rounds. The shared file contains X and Y data. X represents gait data and y represents person information for the relevant sample.

Attribute Information:

Gait Data consists of the following parameters.
Basic Parameters (Speed, Variability, Symmetry), Temporary Parameters (Heel Press Time, Cycle Time, Cadence, Posture, Oscillation, Loading, Foot Press, Thrust, Double Support), Spatial Parameters (Step Length, Step Speed, Peak Angle Speed, Maximum Swing Speed, Rotation Angle, Step Angle, Lift Angle, Swing Width, 3D Path Length), Height Parameters (Maximum Heel Height, Maximum Finger Tip Height, Minimum Finger Tip Height)

Relevant Papers:

Gumuscu A . 'Improvement of Wearable Gait Analysis Sensor based Human Classification using Feature Selection Algorithms '. Firat University Journal of Engineering Science 31 (2019 ): 463-471. ([Web Link])
A. Gumuscu, K. Karadag, M. Caliskan, M. E. Tenekeci and D. Akaslan, 'Gender classification via wearable gait analysis sensor,' 2018 26th Signal Processing and Communications Applications Conference (SIU), Izmir, 2018, pp. 1-4.

Citation Request:

Gumuscu A . 'Improvement of Wearable Gait Analysis Sensor based Human Classification using Feature Selection Algorithms '. Firat University Journal of Engineering Science 31 (2019 ): 463-471. ([Web Link])

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