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Related Experiment Video

Updated: May 1, 2026

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Lower Limb Movement during 10-Meter Walk Test: A Dataset with Inertial, Magnetic, and Temperature Sensors.

Maykol Santos1, Andrés Caro Lindo2, Carlos Albuquerque3,4,5

  • 1Instituto de Telecomunicações, Escola Superior de Tecnologia e Gestão de Águeda, Universidade de Aveiro, Aveiro, Portugal.

Scientific Data
|March 29, 2025
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Summary

This study presents a new dataset on lower limb movement during a 10-meter walk test, captured using wearable sensors. The data links detailed biomechanical insights with participant lifestyle factors for enhanced health monitoring research.

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Area of Science:

  • Biomechanics
  • Rehabilitation Technology
  • Wearable Sensor Technology

Background:

  • Assessing lower limb movement is crucial for evaluating mobility and health, especially in aging populations.
  • Traditional methods often lack objective, continuous, and detailed movement data.
  • Wearable sensors offer a promising solution for capturing in-situ biomechanical and environmental data.

Purpose of the Study:

  • To present a comprehensive dataset of lower limb movement during a 10-meter walk test.
  • To correlate detailed sensor-based movement data with demographic and lifestyle factors.
  • To provide a valuable resource for research in biomechanics, rehabilitation, and health monitoring.

Main Methods:

  • Utilized SensorTileBox sensors integrated into shin pads to collect 3D accelerometer, 3D gyroscope, magnetometer, and temperature data.
  • Developed a custom application to initiate sensor readings during the 10-meter walk test.
  • Collected anonymized participant data including age, gender, exercise habits, diet, and health conditions via a YML form.

Main Results:

  • Generated a time-stamped dataset of lower limb movement parameters during a standardized walk test.
  • Integrated sensor data with contextual demographic and lifestyle information for each participant.
  • Organized data in CSV format within participant-specific, anonymized folders.

Conclusions:

  • The dataset provides a rich resource for analyzing movement patterns in relation to physiological and lifestyle factors.
  • Facilitates research into biomechanics, rehabilitation strategies, and sensor-based health monitoring, particularly for elderly individuals.
  • Enables advanced studies on the interplay between movement, health status, and lifestyle choices.