Abstract
AbstractDifferent technologies can acquire data for gait analysis, such as optical systems and inertial measurement units (IMUs). Each technology has its drawbacks and advantages, fitting best to particular applications. The presented multi-sensor human gait dataset comprises synchronized inertial and optical motion data from 25 participants free of lower-limb injuries, aged between 18 and 47 years. A smartphone and a custom micro-controlled device with an IMU were attached to one of the participant’s legs to capture accelerometer and gyroscope data, and 42 reflexive markers were taped over the whole body to record three-dimensional trajectories. The trajectories and inertial measurements were simultaneously recorded and synchronized. Participants were instructed to walk on a straight-level walkway at their normal pace. Ten trials for each participant were recorded and pre-processed in each of two sessions, performed on different days. This dataset supports the comparison of gait parameters and properties of inertial and optical capture systems, whereas allows the study of gait characteristics specific for each system.
Funder
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Fundação de Amparo à Pesquisa do Estado de São Paulo
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Statistics, Probability and Uncertainty,Computer Science Applications,Education,Information Systems,Statistics and Probability
Reference36 articles.
1. Baker, R. The history of gait analysis before the advent of modern computers. Gait & posture 26, 331–342 (2007).
2. Muro-De-La-Herran, A., Garcia-Zapirain, B. & Mendez-Zorrilla, A. Gait analysis methods: An overview of wearable and non-wearable systems, highlighting clinical applications. Sensors 14, 3362–3394 (2014).
3. Gouwanda, D. & Senanayake, S. Emerging trends of body-mounted sensors in sports and human gait analysis. In 4th Kuala Lumpur Internation‘al Conference on Biomedical Engineering, 715–718 (Springer, 2008).
4. Winter, D. Biomechanics and motor control of human movement, vol. 4th edition (John Wiley & Sons, 2009).
5. Morris, R. & Lawson, S. A review and evaluation of available gait analysis technologies, and their potential for the measurement of impact transmission. Newcastle University (2010).
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