One Small Step for a Man: Estimation of Gender, Age, and Height from Recordings of One Step by a Single Inertial Sensor

In: Sensors (Dez. 2015), 15:12(31999-32019)
 

Abstract

A number of previous works have shown that information about a subject is encoded in sparse kinematic information, such as the one revealed by so-called point light walkers. With the work at hand, we extend these results to classifications of soft biometrics from inertial sensor recordings at a single body location from a single step. We recorded accelerations and angular velocities of 26 subjects using integrated measurement units (IMUs) attached at four locations (chest, lower back, right wrist and left ankle) when performing standardized gait tasks. The collected data were segmented into individual walking steps. We trained random forest classifiers in order to estimate soft biometrics (gender, age and height). We applied two different validation methods to the process, 10-fold cross-validation and subject-wise cross-validation. For all three classification tasks, we achieve high accuracy values for all four sensor locations. From these results, we can conclude that the data of a single walking step (6D: accelerations and angular velocities) allow for a robust estimation of the gender, height and age of a person.

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Bibtex

@ARTICLE{riaz2015c,
    author = {Riaz, Qaiser and V{\"o}gele, Anna and Kr{\"u}ger, Bj{\"o}rn and Weber, Andreas},
     pages = {31999--32019},
     title = {One Small Step for a Man: Estimation of Gender, Age, and Height from  Recordings of One Step by a
              Single Inertial Sensor},
   journal = {Sensors},
    volume = {15},
    number = {12},
      year = {2015},
     month = dec,
  abstract = {A number of previous works have shown that information about a subject is encoded in sparse
              kinematic information, such as the one revealed by so-called point light walkers. With the work at
              hand, we extend these results to classifications of soft biometrics from inertial sensor recordings
              at a single body location from a single step. We recorded accelerations and angular velocities of 26
              subjects using integrated measurement units (IMUs) attached at four locations (chest, lower back,
              right wrist and left ankle) when performing standardized gait tasks. The collected data were
              segmented into individual walking steps. We trained random forest classifiers in order to estimate
              soft biometrics (gender, age and height). We applied two different validation methods to the
              process, 10-fold cross-validation and subject-wise cross-validation. For all three classification
              tasks, we achieve high accuracy values for all four sensor locations. From these results, we can
              conclude that the data of a single walking step (6D: accelerations and angular velocities) allow for
              a robust estimation of the gender, height and age of a person.},
       doi = {10.3390/s151229907}
}