Literature DB >> 28279850

Accuracy and repeatability of single-pose calibration of inertial measurement units for whole-body motion analysis.

Xavier Robert-Lachaine1, Hakim Mecheri2, Christian Larue2, André Plamondon2.   

Abstract

Portable inertial measurement units (IMUs) are suitable for motion analysis outside the laboratory. However, IMUs depend on the calibration of each body segment to measure human movement. Different calibration approaches have been developed for simplicity of use or similarity to laboratory motion analysis, but they have not been extensively examined. The main objective of the study was to determine the accuracy and repeatability of two common single-pose calibrations (N-pose and T-pose) under different conditions of placement (self-placement and passive placement), as well as their similarity to laboratory analysis based on anatomical landmarks. A further aim of the study was to develop two additional single-pose calibrations (chair-pose and stool-pose) and determine their accuracy and repeatability. Postures and movements of 12 healthy participants were recorded simultaneously with a full-body IMU suit and an optoelectronic system as the criterion measure. Three repetitions of the T-pose and the N-pose were executed by self-placement and passive placement, and three repetitions of the chair-pose and stool-pose were also performed. Repeatability for each single-pose calibration showed an average intraclass correlation coefficient for all axes and joints between 0.90 and 0.94 and a standard error of measurement between 1.5° and 2.1°. The T-pose with passive placement is recommended to reduce longitudinal axis offset error and to increase similarity to laboratory motion analysis. Finally, the chair-pose obtained the least longitudinal axis offset error amongst the tested poses, which shows potential for IMU calibration.
Copyright © 2017 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Error; Inertial sensor; Kinematics; Posture; Precision; Similarity

Mesh:

Year:  2017        PMID: 28279850     DOI: 10.1016/j.gaitpost.2017.02.029

Source DB:  PubMed          Journal:  Gait Posture        ISSN: 0966-6362            Impact factor:   2.840


  14 in total

1.  Prediction of lower limb joint angles and moments during gait using artificial neural networks.

Authors:  Marion Mundt; Wolf Thomsen; Tom Witter; Arnd Koeppe; Sina David; Franz Bamer; Wolfgang Potthast; Bernd Markert
Journal:  Med Biol Eng Comput       Date:  2019-12-11       Impact factor: 2.602

2.  IMU-based sensor-to-segment multiple calibration for upper limb joint angle measurement-a proof of concept.

Authors:  Mahdi Zabat; Amina Ababou; Noureddine Ababou; Raphaël Dumas
Journal:  Med Biol Eng Comput       Date:  2019-08-30       Impact factor: 2.602

3.  Barriers to the Adoption of Wearable Sensors in the Workplace: A Survey of Occupational Safety and Health Professionals.

Authors:  Mark C Schall; Richard F Sesek; Lora A Cavuoto
Journal:  Hum Factors       Date:  2018-01-10       Impact factor: 3.598

Review 4.  Measurement of Upper Limb Range of Motion Using Wearable Sensors: A Systematic Review.

Authors:  Corrin P Walmsley; Sîan A Williams; Tiffany Grisbrook; Catherine Elliott; Christine Imms; Amity Campbell
Journal:  Sports Med Open       Date:  2018-11-29

5.  Validity of inertial sensor based 3D joint kinematics of static and dynamic sport and physiotherapy specific movements.

Authors:  Wolfgang Teufl; Markus Miezal; Bertram Taetz; Michael Fröhlich; Gabriele Bleser
Journal:  PLoS One       Date:  2019-02-28       Impact factor: 3.240

6.  Towards an Inertial Sensor-Based Wearable Feedback System for Patients after Total Hip Arthroplasty: Validity and Applicability for Gait Classification with Gait Kinematics-Based Features.

Authors:  Wolfgang Teufl; Bertram Taetz; Markus Miezal; Michael Lorenz; Juliane Pietschmann; Thomas Jöllenbeck; Michael Fröhlich; Gabriele Bleser
Journal:  Sensors (Basel)       Date:  2019-11-16       Impact factor: 3.576

7.  Discriminant validity of 3D joint kinematics and centre of mass displacement measured by inertial sensor technology during the unipodal stance task.

Authors:  R van der Straaten; M Wesseling; I Jonkers; B Vanwanseele; A K B D Bruijnes; J Malcorps; J Bellemans; J Truijen; L De Baets; A Timmermans
Journal:  PLoS One       Date:  2020-05-14       Impact factor: 3.240

8.  Inertial Sensor-Based Motion Analysis of Lower Limbs for Rehabilitation Treatments.

Authors:  Tongyang Sun; Hua Li; Quanquan Liu; Lihong Duan; Meng Li; Chunbao Wang; Qihong Liu; Weiguang Li; Wanfeng Shang; Zhengzhi Wu; Yulong Wang
Journal:  J Healthc Eng       Date:  2017-07-05       Impact factor: 2.682

9.  Reliability of 3D Lower Extremity Movement Analysis by Means of Inertial Sensor Technology during Transitional Tasks.

Authors:  Rob van der Straaten; Annick Timmermans; Amber K B D Bruijnes; Benedicte Vanwanseele; Ilse Jonkers; Liesbet De Baets
Journal:  Sensors (Basel)       Date:  2018-08-11       Impact factor: 3.576

10.  A survey of human shoulder functional kinematic representations.

Authors:  Rakesh Krishnan; Niclas Björsell; Elena M Gutierrez-Farewik; Christian Smith
Journal:  Med Biol Eng Comput       Date:  2018-10-26       Impact factor: 2.602

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