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Design Example: Resistive Touchscreen01:14

Design Example: Resistive Touchscreen

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When a user touches the screen, the two layers make contact at a specific point known as the touchpoint. This contact reduces the resistance between...

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A Tactile Automated Passive-Finger Stimulator TAPS
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Dataset of Smartphone-Based Finger Tapping Test.

Ramon Costa Lima1, Felipe André da Costa Brito1, Rodrigo Luz do Nascimento1

  • 1Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, Brazil.

Scientific Data
|November 21, 2024
PubMed
Summary

This study created a national database of smartphone-based finger tapping tests (FTT) for 176 healthy adults. The database captures detailed motor performance data to support future research on neurological and motor assessments.

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Area of Science:

  • Neuroscience
  • Human-Computer Interaction
  • Biomedical Engineering

Background:

  • The finger tapping test (FTT) is a standard neuropsychological tool for assessing motor speed and coordination.
  • Smartphones offer a novel platform for FTT, enabling objective data capture of tap timing and spatial information.
  • Existing FTT research lacks a comprehensive, standardized national database for healthy adult populations.

Purpose of the Study:

  • To establish a national database of smartphone-based FTT data from a diverse adult sample.
  • To collect detailed information on motor performance using touchscreen technology.
  • To provide a foundation for normative data and comparative studies in FTT research.

Main Methods:

  • Recruited 176 healthy adults aged 18-74 years.
  • Utilized a custom Android application to record FTT performance on a smartphone touchscreen over 30 seconds.
  • Collected data including tap timing, spatial coordinates, participant demographics (age, sex), and device information (smartphone model, hand used).

Main Results:

  • Successfully constructed a database with comprehensive FTT performance metrics from a healthy adult cohort.
  • The data includes individual tap characteristics and demographic variables.
  • Developed a Python-based web tool for visualizing and analyzing individual and aggregated FTT data.

Conclusions:

  • The smartphone-based FTT database provides a valuable resource for understanding typical motor performance in healthy adults.
  • This initiative facilitates further research into neurological conditions and supports international comparative FTT analyses.
  • The collected data can inform the development of more sophisticated digital biomarkers for motor function.