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Using Wearable Sensors for Sex Classification and Age Estimation from Walking Patterns.

Rizvan Jawad Ruhan1, Tahsin Wahid1, Ashikur Rahman1

  • 1Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh.

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|September 19, 2025
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Summary

Gait analysis using inertial sensors can predict sex with 94% accuracy and estimate age. This study identifies key gait features for improved biometric analysis in humans.

Keywords:
age estimationgait analysismachine learningmotion trackingsex classificationsmartphonewearable sensor

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Area of Science:

  • Biometrics
  • Human-computer interaction
  • Machine learning

Background:

  • Gait, a unique walking pattern, serves as a biometric identifier.
  • Traditional gait analysis for identification faces scalability challenges with large databases.
  • Research is shifting towards using gait for demographic prediction, including sex and age.

Purpose of the Study:

  • To propose and analyze novel gait features from inertial sensor data.
  • To investigate the effectiveness of these features for automatic sex recognition and age prediction.
  • To evaluate traditional machine learning models for these tasks.

Main Methods:

  • Extraction of time-series features from accelerometer and gyroscope data.
  • Application of various traditional machine learning models for classification and regression.
  • Analysis of key features for their contribution to prediction accuracy.

Main Results:

  • Achieved 94% accuracy in sex prediction.
  • Obtained an R2 score of 0.83 for age estimation.
  • Identified significant gait features for demographic prediction.

Conclusions:

  • Gait analysis using inertial sensors is effective for sex and age prediction.
  • Feature engineering from time-series gait data is crucial for performance.
  • Machine learning models can reliably predict demographic attributes from walking patterns.