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<p>This paper presents a dataset from a study analyzing lower limb movement during a 10-meter walk test. The study utilized SensorTileBox sensors integrated into shin pads to capture detailed movement and environmental data from participants. The sensors recorded 3D accelerometer data (in milligravities), 3D gyroscope data (degrees per second), magnetometer readings (milligauss), and temperature (°C). The dataset was collected through a custom-made application that initiated the sensor readings while the patient performed the walk test. Alongside sensor data, additional demographic and health information, including age, gender, physical exercise habits, diet, and health conditions, were collected via a form stored in a YML file. This information provides context for the sensor measurements and allows for comprehensive analysis. All sensor measurements are time-stamped and stored in CSV format, with participant-specific data anonymized and organized in folders by numeric identifiers. This dataset offers a valuable resource for studying movement patterns in relation to physiological and lifestyle factors, particularly for elderly individuals. It could support research in biomechanics, rehabilitation, and sensor-based health monitoring.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月29日

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  • Maykol Santos,
  • Andrés Caro Lindo,
  • Carlos Albuquerque,
  • Paulo Jorge Coelho,
  • Ivan Miguel Pires

摘要

This paper presents a dataset from a study analyzing lower limb movement during a 10-meter walk test. The study utilized SensorTileBox sensors integrated into shin pads to capture detailed movement and environmental data from participants. The sensors recorded 3D accelerometer data (in milligravities), 3D gyroscope data (degrees per second), magnetometer readings (milligauss), and temperature (°C). The dataset was collected through a custom-made application that initiated the sensor readings while the patient performed the walk test. Alongside sensor data, additional demographic and health information, including age, gender, physical exercise habits, diet, and health conditions, were collected via a form stored in a YML file. This information provides context for the sensor measurements and allows for comprehensive analysis. All sensor measurements are time-stamped and stored in CSV format, with participant-specific data anonymized and organized in folders by numeric identifiers. This dataset offers a valuable resource for studying movement patterns in relation to physiological and lifestyle factors, particularly for elderly individuals. It could support research in biomechanics, rehabilitation, and sensor-based health monitoring.