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Bogazici university smartphone accelerometer sensor dataset.

Erhan Davarcı¹, Emin Anarım¹

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

|March 4, 2022

Summary

This summary is machine-generated.

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

Keywords:

Accelerometer Age-group detection Authentication Behavioral biometrics Gender recognition Motion sensors Smartphones

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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).
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.