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Related Experiment Videos

Bogazici university smartphone accelerometer sensor dataset.

Erhan Davarcı1, Emin Anarım1

  • 1Bogazici University Electrical and Electronics Engineering, Istanbul, Turkey.

Data in Brief
|March 4, 2022
PubMed
Summary
This summary is machine-generated.

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Smartphone sensor data reveals distinct user behaviors. Accelerometer data can differentiate age groups, genders, and activities, enabling behavioral biometrics for user identification and authentication.

Area of Science:

  • Human-Computer Interaction
  • Mobile Computing
  • Biometrics

Background:

  • Smartphones are ubiquitous, making user interaction analysis crucial.
  • Motion sensors (accelerometer, gyroscope) capture behavioral data.
  • Understanding behavioral differences is key for personalized and secure mobile experiences.

Purpose of the Study:

  • To develop an Android application for collecting accelerometer and touch event data.
  • To analyze how age group, gender, and activity type influence smartphone interaction patterns.
  • To explore the potential of sensor data for behavioral biometric applications.

Main Methods:

  • Developed an Android app to record accelerometer and touch data during various user activities.
  • Conducted two experiments: one comparing child and adult users (107 children, 100 adults), another comparing genders during sitting and walking (60 females, 60 males).
Keywords:
AccelerometerAge-group detectionAuthenticationBehavioral biometricsGender recognitionMotion sensorsSmartphones

Related Experiment Videos

  • Collected over 11,000 taps for age analysis and over 6,000 taps for gender/activity analysis.

    • Main Results:

    • Accelerometer data patterns differ significantly between age groups.
    • Gender and activity type (e.g., sitting vs. walking) also create detectable variations in sensor data.
    • The collected datasets provide a foundation for behavioral biometric analyses.

    Conclusions:

    • Smartphone sensor data offers valuable insights into user behavior.
    • This data can be effectively utilized for user age-group and gender detection.
    • The findings support the application of behavioral biometrics for enhanced user identification and authentication on mobile devices.