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A Quaternion-Based Method to IMU-to-Body Alignment for Gait Analysis

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Resumen

Human gait analysis based on inertial measurement units (IMUs) is still considered a challenging task. This is because the accurate capture of human body movements depends on an initial sensor-to-body calibration and alignment process. In this paper, a novel sensor-to-body alignment method based on sequences of quaternions is presented, which allows to accurately estimate the joint angles from the hip, knee and ankle of the lower limbs. The proposed method involves two main stages, a sensors calibration and an alignment process for the body segments, respectively. For doing that, two different sequences of rotation based on Euler angle-axis factors are developed. The first rotational sequence is used to calibrate sensor’s frame under a new general body frame by estimating the initial orientation based on its quaternion information. Then, a correction process is applied by factorizing the captured quaternions. Once the general body frame is defined, a second rotational sequence is implemented, which aligns each sensor frame to body frames, allowing to define the anatomic frames for obtaining clinical measurements of the joint angles. The proposed method was two-fold validated using both strategies, a goniometer-based measure system and a camera-based motion system, respectively. The obtained results demonstrate that the estimated joint angles are equal to the expected values and consistent with values obtained by the strategies widely used in real clinical scenarios, the goniometers and optical motion system. Therefore, the proposed method could be used in clinical applications and motion analysis of impaired persons.

Idioma originalInglés
Título de la publicación alojadaDigital Human Modeling. Applications in Health, Safety, Ergonomics, and Risk Management - 9th International Conference, DHM 2018, Held as Part of HCI International 2018, Proceedings
EditoresVincent G. Duffy
EditorialSpringer Verlag
Páginas217-231
Número de páginas15
ISBN (versión impresa)9783319913964
DOI
EstadoPublicada - 2018
Evento9th International Conference on Digital Human Modeling, DHM 2018 Held as Part of HCI International 2018 - Las Vegas, Estados Unidos
Duración: 15 jul. 201820 jul. 2018

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen10917 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia9th International Conference on Digital Human Modeling, DHM 2018 Held as Part of HCI International 2018
País/TerritorioEstados Unidos
CiudadLas Vegas
Período15/07/1820/07/18

Nota bibliográfica

Publisher Copyright:
© 2018, Springer International Publishing AG, part of Springer Nature.

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