ArticleScientific data2025
Lower Limb Movement during 10-Meter Walk Test: A Dataset with Inertial, Magnetic, and Temperature Sensors.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Spatiotemporal and foot kinematic differences during gait in individuals with cavus foot.Scientific reports · 2025Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
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