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Related Concept Videos

Design Example01:23

Design Example

The innovation of touch-tone telephony revolutionized the telecommunications industry by replacing the traditional rotary dial with a dual-tone multi-frequency (DTMF) signaling system. This system uses a matrix-style keypad with buttons arranged in four rows and three columns, creating 12 distinct signals each assigned to a pair of frequencies. Each button press results in a simultaneous generation of two sinusoidal tones – one from a low-frequency group (697 to 941 Hz) and one from a...
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Related Experiment Video

Updated: Jun 12, 2026

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study
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Exploring Age-Related Patterns in Smartphone Keystroke Dynamics Considering Temporal Variability: Cross-Sectional

Junhyung Moon1, Yu Lim Huh2, Hee Young Cho3,4

  • 1Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongnam, Republic of Korea.

JMIR Mhealth and Uhealth
|December 2, 2025
PubMed
Summary
This summary is machine-generated.

Smartphone typing patterns reveal age-related differences, with younger individuals typing faster and older individuals typing slower. These keystroke dynamics can serve as passive digital biomarkers for age-related functional changes.

Keywords:
age-related behaviorartificial intelligencedigital biomarkerskeystroke dynamicsmobile healthmobile phonepassive sensingtemporal variability

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Area of Science:

  • Digital biomarkers
  • Human-computer interaction
  • Gerontology

Background:

  • Keystroke dynamics on smartphones offer a promising avenue for passive digital biomarkers.
  • Previous research has explored keystroke dynamics in various diseases, but less is known about age-related changes in the general population.

Purpose of the Study:

  • To investigate age-related patterns in mobile keystroke dynamics, focusing on daily temporal variations.
  • To identify behavioral signatures specific to different age groups.
  • To evaluate the accuracy of artificial intelligence (AI) models in estimating chronological age from keystroke data.

Main Methods:

  • A field study collected typing data from 177 healthy adults in South Korea using a custom Android keyboard app over several weeks.
  • 43 behavioral features (speed, frequency, variability) were extracted from keystroke timestamps and key types.
  • Multiple AI models, including LSTM and Transformer, were trained using 6-hour, daily, and weekly data resolutions, with a custom loss function to minimize prediction variability.

Main Results:

  • Descriptive analysis showed younger participants typed faster and more frequently than older participants, who exhibited slower and more variable typing.
  • The Long Short-Term Memory (LSTM) model achieved the best age estimation (Mean Absolute Error: 3.69 years, R²=0.71), improved to 3.60 MAE with a custom loss function.
  • Feature importance indicated that early morning and late evening typing patterns were most discriminative of age.

Conclusions:

  • Smartphone keystroke dynamics exhibit age-sensitive behavioral patterns, especially with fine-grained temporal analysis.
  • These dynamics show potential as passive, unobtrusive markers for age-related functional characteristics.
  • Findings may support future digital health applications for age-sensitive personalization and early detection of age-related decline.