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

Robert-Lachaîne, Xavier; Mecheri, Hakim; Larue, Christian; Plamondon, André

doi:10.1016/j.gaitpost.2017.02.029

Cited by 81 publications

(80 citation statements)

References 26 publications

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“…On the other hand, the hip F/E offset can be mostly contributed to an offset between different approaches (MVN, PiG and OS) as it is of similar size as the RMSD and correlations are excellent (ρ > 0.98). MVN shows the largest offset compared to both PiG and OS, since this offset is observed consistently for all measured subjects, it is likely caused by the calibration procedure [8], [9]. Pelvic tilt can differ between subjects in the calibration pose while identical assumptions about that pose are still applied to each subject.…”

Section: Discussionmentioning

confidence: 79%

“…Both inertial-magnetic and optical motion capture accuracy can be influenced by different external factors, e.g. soft-tissue artifacts (STAs) [5], occlusions [6], sensor/marker placement [7], [8], calibration pose accuracy [8], [9] and/or magnetic disturbances [3], [10]. Most of those potential sources of errors can be solved by proper measurement preparation, e.g.…”

Section: Introductionmentioning

confidence: 99%

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On the Validity of Different Motion Capture Technologies for the Analysis of Running

Wouda

Giuberti

Bellusci

et al. 2018

2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)

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An increasing diversity of available motion capture technologies allows for measurement of human kinematics in various environments. However, little is known about the differences in quality of measured kinematics by such technologies. Therefore, this work presents a comparison between three motion capture approaches, based on inertial-magnetic measurement units (processed with Xsens MVN Analyze) and optical markers (processed using Plug-In Gait and OpenSim Gait2392). It was chosen to evaluate the different motion capture approaches in running, as such kinematics are preferably measured in the natural running environment and involve challenging dynamics. An evaluation was done using data of 8 subjects running on a treadmill at three different speeds, namely 10, 12 and 14 km/h. The sagittal plane results show excellent correlation (ρ > 0.96) and RMSDs are smaller than 5 degrees for 6 out of the 8 subjects. However, results in the frontal and transversal planes were less correlated between the different motion capture approaches. This shows that sagittal kinematics can be measured consistently using any of the three analyzed motion capture approaches, but ambiguities exist in the analysis of frontal and transversal planes.

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Section: Discussionmentioning

confidence: 79%

Section: Introductionmentioning

confidence: 99%

On the Validity of Different Motion Capture Technologies for the Analysis of Running

Wouda

Giuberti

Bellusci

et al. 2018

2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)

View full text Add to dashboard Cite

show abstract

“…Different attempts have been made either correcting magnetic disturbances or omitting the use of magnetometers at all (Ligorio and Sabatini, 2016;Teufl et al, 2018). However, another major issue of the commonly used approach is the (mal-)alignment of the sensor axes to physiological meaningful segment and rotation axes that define the anatomical model (Picerno, 2017;Robert-Lachaine et al, 2017;Mundt et al, 2019d). Several approaches have been suggested to overcome this problem: calibration postures or movements (Favre et al, 2009;Ferrari et al, 2010;Palermo et al, 2014), anatomical calibrations (Picerno et al, 2008;Bisi et al, 2015), post-trial calibration procedures (Hamacher et al, 2014;Li and Zhang, 2014) and more recently machine learning approaches (Zimmermann et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Estimation of Gait Mechanics Based on Simulated and Measured IMU Data Using an Artificial Neural Network

Mundt

Koeppe

David

et al. 2020

Front. Bioeng. Biotechnol.

122

104

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“…Recently, inertial sensor has been used to detect and evaluate human motion: whole body motion [12, 13], scapula calibration [14], lie-to-stand transfer [15], and gait analysis [16–20]. The studies using inertial sensors measure movement time; calculate joint or inclination angles, walking speed, step or stride length, and segment position relative to other position; and detect gait event timings.…”

Section: Introductionmentioning

confidence: 99%

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

Sun

Liu

et al. 2017

Journal of Healthcare Engineering

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The hemiplegic rehabilitation state diagnosing performed by therapists can be biased due to their subjective experience, which may deteriorate the rehabilitation effect. In order to improve this situation, a quantitative evaluation is proposed. Though many motion analysis systems are available, they are too complicated for practical application by therapists. In this paper, a method for detecting the motion of human lower limbs including all degrees of freedom (DOFs) via the inertial sensors is proposed, which permits analyzing the patient's motion ability. This method is applicable to arbitrary walking directions and tracks of persons under study, and its results are unbiased, as compared to therapist qualitative estimations. Using the simplified mathematical model of a human body, the rotation angles for each lower limb joint are calculated from the input signals acquired by the inertial sensors. Finally, the rotation angle versus joint displacement curves are constructed, and the estimated values of joint motion angle and motion ability are obtained. The experimental verification of the proposed motion detection and analysis method was performed, which proved that it can efficiently detect the differences between motion behaviors of disabled and healthy persons and provide a reliable quantitative evaluation of the rehabilitation state.

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

Cited by 81 publications

References 26 publications

On the Validity of Different Motion Capture Technologies for the Analysis of Running

On the Validity of Different Motion Capture Technologies for the Analysis of Running

Estimation of Gait Mechanics Based on Simulated and Measured IMU Data Using an Artificial Neural Network

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

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