Self-Calibrating Magnetometer-Free Inertial Motion Tracking of 2-DoF Joints.

anatomical calibration elbow joint human motion analysis inertial measurement unit inertial sensor kinematic constraints sensor-to-segment calibration

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
15 Dec 2022
Historique:
received: 31 10 2022
revised: 05 12 2022
accepted: 12 12 2022
entrez: 23 12 2022
pubmed: 24 12 2022
medline: 27 12 2022
Statut: epublish

Résumé

Human motion analysis using inertial measurement units (IMUs) has recently been shown to provide accuracy similar to the gold standard, optical motion capture, but at lower costs and while being less restrictive and time-consuming. However, IMU-based motion analysis requires precise knowledge of the orientations in which the sensors are attached to the body segments. This knowledge is commonly obtained via time-consuming and error-prone anatomical calibration based on precisely defined poses or motions. In the present work, we propose a self-calibrating approach for magnetometer-free joint angle tracking that is suitable for joints with two degrees of freedom (DoF), such as the elbow, ankle, and metacarpophalangeal finger joints. The proposed methods exploit kinematic constraints in the angular rates and the relative orientations to simultaneously identify the joint axes and the heading offset. The experimental evaluation shows that the proposed methods are able to estimate plausible and consistent joint axes from just ten seconds of arbitrary elbow joint motion. Comparison with optical motion capture shows that the proposed methods yield joint angles with similar accuracy as a conventional IMU-based method while being much less restrictive. Therefore, the proposed methods improve the practical usability of IMU-based motion tracking in many clinical and biomedical applications.

Identifiants

pubmed: 36560219
pii: s22249850
doi: 10.3390/s22249850
pmc: PMC9785932
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

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Auteurs

Daniel Laidig (D)

Control Systems Group, Technische Universität Berlin, 10623 Berlin, Germany.

Ive Weygers (I)

Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, Germany.

Thomas Seel (T)

Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, Germany.

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