» Articles » PMID: 36997555

A Dataset of Asymptomatic Human Gait and Movements Obtained from Markers, IMUs, Insoles and Force Plates

Overview
Journal Sci Data
Specialty Science
Date 2023 Mar 30
PMID 36997555
Authors
Affiliations
Soon will be listed here.
Abstract

Human motion capture and analysis could be made easier through the use of wearable devices such as inertial sensors and/or pressure insoles. However, many steps are still needed to reach the performance of optoelectronic systems to compute kinematic parameters. The proposed dataset has been established on 10 asymptomatic adults. Participants were asked to walk at different speeds on a 10-meters walkway in a laboratory and to perform different movements such as squats or knee flexion/extension tasks. Three-dimensional trajectories of 69 reflective markers placed according to a conventional full body markerset, acceleration and angular velocity signals of 8 inertial sensors, pressure signals of 2 insoles, 3D ground reaction forces and moments obtained from 3 force plates were simultaneously recorded. Eight calculated virtual markers related to joint centers were also added to the dataset. This dataset contains a total of 337 trials including static and dynamic tasks for each participant. Its purpose is to enable comparisons between various motion capture systems and stimulate the development of new methods for gait analysis.

Citing Articles

A dataset of optical camera and IMU sensor derived kinematics of thirty transtibial prosthesis wearers.

Samala M, Rattanakoch J, Guerra G, Tharawadeepimuk K, Nanbancha A, Niamsang W Sci Data. 2024; 11(1):922.

PMID: 39181912 PMC: 11344789. DOI: 10.1038/s41597-024-03677-3.


3D motion analysis dataset of healthy young adult volunteers walking and running on overground and treadmill.

Riglet L, Delphin C, Claquesin L, Orliac B, Ornetti P, Laroche D Sci Data. 2024; 11(1):556.

PMID: 38816523 PMC: 11139954. DOI: 10.1038/s41597-024-03420-y.

References
1.
Lebleu J, Gosseye T, Detrembleur C, Mahaudens P, Cartiaux O, Penta M . Lower Limb Kinematics Using Inertial Sensors during Locomotion: Accuracy and Reproducibility of Joint Angle Calculations with Different Sensor-to-Segment Calibrations. Sensors (Basel). 2020; 20(3). PMC: 7039222. DOI: 10.3390/s20030715. View

2.
Olivares A, Ramirez J, Gorriz J, Olivares G, Damas M . Detection of (in)activity periods in human body motion using inertial sensors: a comparative study. Sensors (Basel). 2012; 12(5):5791-814. PMC: 3386712. DOI: 10.3390/s120505791. View

3.
Leboeuf F, Baker R, Barre A, Reay J, Jones R, Sangeux M . The conventional gait model, an open-source implementation that reproduces the past but prepares for the future. Gait Posture. 2019; 69:235-241. DOI: 10.1016/j.gaitpost.2019.04.015. View

4.
de Melo T, Duarte A, Bezerra T, Franca F, Soares N, Brito D . The Five Times Sit-to-Stand Test: safety and reliability with older intensive care unit patients at discharge. Rev Bras Ter Intensiva. 2019; 31(1):27-33. PMC: 6443310. DOI: 10.5935/0103-507X.20190006. View

5.
Gorton 3rd G, Hebert D, Gannotti M . Assessment of the kinematic variability among 12 motion analysis laboratories. Gait Posture. 2008; 29(3):398-402. DOI: 10.1016/j.gaitpost.2008.10.060. View