Optimization of Inertial Sensor-Based Motion Capturing for Magnetically Distorted Field Applications

Schiefer, Christoph; Ellegast, Rolf; Hermanns, Ingo; Kraus, Thomas; Ochsmann, Elke; Larue, Christian; Plamondon, André

doi:10.1115/1.4028822

Cited by 32 publications

(23 citation statements)

References 27 publications

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“…Although similar trends have been observed in published Kalman filter designs (Bergamini et al 2014; Gabriele Ligorio and Sabatini 2016), the IMU error magnitudes and characteristics observed in this study are applicable to the specific hardware and Kalman filter specifications (which are proprietary). Other inertial-based motion capture systems may use proprietary filters based on other strategies to minimize the effects of magnetic disturbances, such as zero-velocity updates (Schiefer et al 2014) or dynamics-based kinematic modeling (El-Gohary and McNames 2012). Also, the relationship between magnetic field strength standard deviation and magnetic heading deviation may be different with other sources of magnetic disturbance.…”

Section: Methodological Considerationsmentioning

confidence: 99%

“…Fundamentally, the spatial orientation of an IMU can be calculated using two distinct methods: (i) with respect to its initial orientation by integrating the angular velocity measured by the gyroscope, or (ii) with respect to gravity and magnetic north using an accelerometer and magnetometer (Yun, Bachmann, and McGhee 2008;Valenti, Dryanovski, and Xiao 2015;Bergamini et al 2014;Schiefer et al 2014).…”

Section: Imu Operationmentioning

confidence: 99%

“…Several studies reported favorable results (<6° average error) (Plamondon et al 2007;Faber et al 2013; Kim and Nussbaum 2013;Martori et al 2013;Bergamini et al 2014), while others have reported inconsistent IMU error. For example, Schiefer et al, (2014) reported average root-mean-square (RMS) errors of 8.3° for the thorax and 8.6° for the left upper arm, but 14.5° for the right forearm and 16.8° for the head. Cloete et al, (2008) reported average RMS errors of 6.5° for right hip rotation, but 18.8° for right ankle rotation.…”

Section: Assessing Imu Accuracymentioning

confidence: 99%

“…In addition, researchers often evaluate IMUs under nominal conditions with precautionary measures to control for magnetic disturbance (i.e. maintaining set distance between the system and known sources of disturbances) (Robert-Lachaine et al 2016; Kim and Nussbaum 2013;Lebel et al 2013;Lebel et al 2015;Schiefer et al 2014), which limits study generalizability to the laboratory environment (Schiefer et al 2014). The results may also be confounded by unforeseen sources of magnetic disturbance (e.g.…”

Section: Gaps In Current Literaturementioning

confidence: 99%

“…However, IMU accuracy has been observed to be negatively affected by magnetic field disturbances (de Vries et al 2009;Bachmann, Yun, and Peterson 2004) and highly dynamic motion (Lebel et al 2013;Ricci, Taffoni, and Formica 2016). Because disturbances of the local magnetic field can cause directional changes up to 180° (Bachmann, Yun, and Peterson 2004), IMU accuracy is typically evaluated using procedures to minimize this problem (Kim and Nussbaum 2013;Lebel et al 2015;Lebel et al 2013;Robert-Lachaine et al 2016;Schiefer et al 2014).…”

Section: Introductionmentioning

confidence: 99%

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The effects of movement speeds and magnetic disturbance on inertial measurement unit accuracy

Chen¹

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There are countless individuals who have supported me throughout my time as a graduate student. This journey would probably not have started if Prof. Geb Thomas did not take me into his research lab early in my sophomore year, a point in time where few other professors would see value in a student, especially one with less-than-stellar academic records. Throughout my time working with him as an undergraduate and master's student, he showed me the path less traveled, taught me how to 'think like an engineer', and gave me the skills to turn ideas into reality. I am also grateful for having him as my academic advisor and committee member throughout my doctoral work. This journey would not have gone smoothly if it wasn't for friends to keep everything in perspective. I would like to thank old friends that I have known as an undergraduate, who still make frequent pilgrimages back to visit, including Stephanie Swanson, Jose Ponce, Michael Carbone, and many others. These are the people that constantly remind me how truly special Iowa City is. I would also like to thank my undergraduate friends that have stayed here for their pursuit of advanced degrees, including

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Section: Methodological Considerationsmentioning

confidence: 99%

Section: Imu Operationmentioning

confidence: 99%

Section: Assessing Imu Accuracymentioning

confidence: 99%

Section: Gaps In Current Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The effects of movement speeds and magnetic disturbance on inertial measurement unit accuracy

Chen¹

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Toward an Affordable Multi-Modal Motion Capture System Framework for Human Kinematics and Kinetics Assessment

Mallat

Bonnet

Khalil

et al. 2018

Biosystems &Amp; Biorobotics

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Quantifying human motor act starts with measuring and estimating kinematics and dynamics variables as accurately as possible. Monitoring human motion has a wide array of applications in functional rehabilitation, orthopaedics, sports, assistive robotics or industrial ergonomics. Today's motion capture systems usually refer to stereophotogrammetric systems and laboratory-grade force-plate that are accurate but also costly, require expert skills, and are not portable. Recently, the use of affordable sensors for human motion estimation, such as Inertial Measurement Unit or RGB-Depth camera(s), has been the subject of numerous studies. Despite their great potential to be used outside of the laboratory, these systems still suffer from limited accuracy, mainly due to inherent IMU drift and visual occlusions, and the joint kinematics and kinetics estimates are still difficult to be estimated. These drawbacks might explain why such systems are rarely used in common clinical applications or for in-home rehabilitation programs. In this context, this thesis deals with the development of a new affordable motion capture system capable of estimating accurately human 3D joint state. Unlike previous studies based on either visual or inertial sensors, the proposed approach consists in combining data from newly designed visual-inertial sensors. The system is also making use of new practical calibration methods, which do not require any external equipment while remaining very affordable. All sensors data are fused into a constrained extended Kalman filter that takes advantage of the biomechanics of the human body and of the investigated tasks to improve significantly joint state estimate. This is done by incorporating different types of constraints, such as joint limits, rigid-body and soft joint constraints, as well as modelling the temporal evolution of joint trajectories and/or sensors random bias. The system's ability to estimate accurate 3D joint kinematics has been validated through various case studies of daily life activities for upper-arm and treadmill gait. Two different prototypes with different sensors count and configurations have been investigated. Experiments conducted with several healthy subjects showed very satisfactory results when compared to a gold standard motion capture system. Overall, the average RMS difference between the two systems was below 4deg. This was also the case when a reduced number of sensors was used for gait analysis. This system was also used for the dynamics identification of a viii lower-limbs human-exoskeleton system. As a result, an error below 6% was observed when comparing estimated and measured external ground reaction force and moments. Finally, beyond these validations, a dynamics assessment framework has been proposed with the aim of selecting an optimal human-exoskeleton dynamic model that is the best trade-off between the accuracy of kinetic estimation, i.e., joint torque, and simplicity of modelling. To this end, the proposed framework consists in quantifying the independent con...

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Validation of inertial measurement units with an optoelectronic system for whole-body motion analysis

Robert-Lachaîne

Mecheri

Larue

et al. 2016

Med Biol Eng Comput

251

246

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The potential of inertial measurement units (IMUs) for ergonomics applications appears promising. However, previous IMUs validation studies have been incomplete regarding aspects of joints analysed, complexity of movements and duration of trials. The objective was to determine the technological error and biomechanical model differences between IMUs and an optoelectronic system and evaluate the effect of task complexity and duration. Whole-body kinematics from 12 participants was recorded simultaneously with a full-body Xsens system where an Optotrak cluster was fixed on every IMU. Short functional movements and long manual material handling tasks were performed and joint angles were compared between the two systems. The differences attributed to the biomechanical model showed significantly greater (P ≤ .001) RMSE than the technological error. RMSE was systematically higher (P ≤ .001) for the long complex task with a mean on all joints of 2.8° compared to 1.2° during short functional movements. Definition of local coordinate systems based on anatomical landmarks or single posture was the most influent difference between the two systems. Additionally, IMUs accuracy was affected by the complexity and duration of the tasks. Nevertheless, technological error remained under 5° RMSE during handling tasks, which shows potential to track workers during their daily labour.

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Optimization of Inertial Sensor-Based Motion Capturing for Magnetically Distorted Field Applications

Cited by 32 publications

References 27 publications

The effects of movement speeds and magnetic disturbance on inertial measurement unit accuracy

The effects of movement speeds and magnetic disturbance on inertial measurement unit accuracy

Toward an Affordable Multi-Modal Motion Capture System Framework for Human Kinematics and Kinetics Assessment

Validation of inertial measurement units with an optoelectronic system for whole-body motion analysis

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