Details
| Original language | English |
|---|---|
| Article number | 208 |
| Journal | Scientific data |
| Volume | 8 |
| Issue number | 1 |
| Early online date | 5 Aug 2021 |
| Publication status | Published - Dec 2021 |
| Externally published | Yes |
Abstract
Skin-attached inertial sensors are increasingly used for kinematic analysis. However, their ability to measure outside-lab can only be exploited after correctly aligning the sensor axes with the underlying anatomical axes. Emerging model-based inertial-sensor-to-bone alignment methods relate inertial measurements with a model of the joint to overcome calibration movements and sensor placement assumptions. It is unclear how good such alignment methods can identify the anatomical axes. Any misalignment results in kinematic cross-talk errors, which makes model validation and the interpretation of the resulting kinematics measurements challenging. This study provides an anatomically correct ground-truth reference dataset from dynamic motions on a cadaver. In contrast with existing references, this enables a true model evaluation that overcomes influences from soft-tissue artifacts, orientation and manual palpation errors. This dataset comprises extensive dynamic movements that are recorded with multimodal measurements including trajectories of optical and virtual (via computed tomography) anatomical markers, reference kinematics, inertial measurements, transformation matrices and visualization tools. The dataset can be used either as a ground-truth reference or to advance research in inertial-sensor-to-bone-alignment.
ASJC Scopus subject areas
- Mathematics(all)
- Statistics and Probability
- Computer Science(all)
- Information Systems
- Social Sciences(all)
- Education
- Computer Science(all)
- Computer Science Applications
- Decision Sciences(all)
- Statistics, Probability and Uncertainty
- Social Sciences(all)
- Library and Information Sciences
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In: Scientific data, Vol. 8, No. 1, 208, 12.2021.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Reference in-vitro dataset for inertial-sensor-to-bone alignment applied to the tibiofemoral joint
AU - Weygers, Ive
AU - Kok, Manon
AU - Seel, Thomas
AU - Shah, Darshan
AU - Taylan, Orçun
AU - Scheys, Lennart
AU - Hallez, Hans
AU - Claeys, Kurt
N1 - Funding Information: This work was supported by the European Regional Development Fund - We-lab for HTM [grant number 1047]. The authors would like to thank Elias Theunynck and Emiel Nieuwlaet for their assistance during data collection.
PY - 2021/12
Y1 - 2021/12
N2 - Skin-attached inertial sensors are increasingly used for kinematic analysis. However, their ability to measure outside-lab can only be exploited after correctly aligning the sensor axes with the underlying anatomical axes. Emerging model-based inertial-sensor-to-bone alignment methods relate inertial measurements with a model of the joint to overcome calibration movements and sensor placement assumptions. It is unclear how good such alignment methods can identify the anatomical axes. Any misalignment results in kinematic cross-talk errors, which makes model validation and the interpretation of the resulting kinematics measurements challenging. This study provides an anatomically correct ground-truth reference dataset from dynamic motions on a cadaver. In contrast with existing references, this enables a true model evaluation that overcomes influences from soft-tissue artifacts, orientation and manual palpation errors. This dataset comprises extensive dynamic movements that are recorded with multimodal measurements including trajectories of optical and virtual (via computed tomography) anatomical markers, reference kinematics, inertial measurements, transformation matrices and visualization tools. The dataset can be used either as a ground-truth reference or to advance research in inertial-sensor-to-bone-alignment.
AB - Skin-attached inertial sensors are increasingly used for kinematic analysis. However, their ability to measure outside-lab can only be exploited after correctly aligning the sensor axes with the underlying anatomical axes. Emerging model-based inertial-sensor-to-bone alignment methods relate inertial measurements with a model of the joint to overcome calibration movements and sensor placement assumptions. It is unclear how good such alignment methods can identify the anatomical axes. Any misalignment results in kinematic cross-talk errors, which makes model validation and the interpretation of the resulting kinematics measurements challenging. This study provides an anatomically correct ground-truth reference dataset from dynamic motions on a cadaver. In contrast with existing references, this enables a true model evaluation that overcomes influences from soft-tissue artifacts, orientation and manual palpation errors. This dataset comprises extensive dynamic movements that are recorded with multimodal measurements including trajectories of optical and virtual (via computed tomography) anatomical markers, reference kinematics, inertial measurements, transformation matrices and visualization tools. The dataset can be used either as a ground-truth reference or to advance research in inertial-sensor-to-bone-alignment.
UR - http://www.scopus.com/inward/record.url?scp=85111994151&partnerID=8YFLogxK
U2 - 10.1038/s41597-021-00995-8
DO - 10.1038/s41597-021-00995-8
M3 - Article
C2 - 34354084
AN - SCOPUS:85111994151
VL - 8
JO - Scientific data
JF - Scientific data
SN - 2052-4463
IS - 1
M1 - 208
ER -