Human Gait Database for Normal Walk Collected by Smart Phone Accelerometer

The goal of this study is to introduce a comprehensive gait database of 93 human subjects who walked between two endpoints during two different sessions and record their gait data using two smartphones, one was attached to the right thigh and another one on the left side of the waist. This data is collected with the intention to be utilized by a deep learning-based method which requires enough time points. The metadata including age, gender, smoking, daily exercise time, height, and weight of an individual is recorded. this data set is publicly available.

[1]  Petia Radeva,et al.  Personalization and user verification in wearable systems using biometric walking patterns , 2011, Personal and Ubiquitous Computing.

[2]  Matteo Gadaleta,et al.  IDNet: Smartphone-based Gait Recognition with Convolutional Neural Networks , 2016, Pattern Recognit..

[3]  Shie Mannor,et al.  A novel similarity measure for time series data with applications to gait and activity recognition , 2010, UbiComp '10 Adjunct.

[4]  Einar Snekkenes,et al.  Gait Authentication and Identification Using Wearable Accelerometer Sensor , 2007, 2007 IEEE Workshop on Automatic Identification Advanced Technologies.

[5]  Thang Hoang,et al.  Gait identification using accelerometer on mobile phone , 2012, 2012 International Conference on Control, Automation and Information Sciences (ICCAIS).

[6]  Xavier Savatier,et al.  Biometric database for human gait recognition using wearable sensors and a smartphone , 2017, 2017 2nd International Conference on Bio-engineering for Smart Technologies (BioSMART).

[7]  Yasushi Makihara,et al.  The largest inertial sensor-based gait database and performance evaluation of gait-based personal authentication , 2014, Pattern Recognit..

[8]  Danilo Gligoroski,et al.  Walk the Walk: Attacking Gait Biometrics by Imitation , 2010, ISC.

[9]  Davrondzhon Gafurov,et al.  A Survey of Biometric Gait Recognition: Approaches, Security and Challenges , 2007 .

[10]  Kôiti Hasida,et al.  Rotation invariant feature extraction from 3-D acceleration signals , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[11]  J. Read,et al.  The Handbook of Eyewitness Psychology: Volume II : Memory for People , 2007 .