Ground reaction force and joint moment estimation during gait using an Azure Kinect-driven musculoskeletal modeling approach

Ripic, Zachary; Kuenze, Christopher; Andersen, Michael Skipper; Theodorakos, Ilias; Signorile, Joseph F.; Eltoukhy, Moataz

doi:10.1016/j.gaitpost.2022.04.005

Cited by 19 publications

(9 citation statements)

References 30 publications

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“…By estimating GRF&Ms solely based on kinematics, without the need for measured force plate data, this approach therefore presents potential to expand biomechanical investigations beyond confined motion laboratories into real-life scenarios ( Larsen et al, 2020 ; Skals et al, 2021 ). Moreover, the approach can be combined with novel motion-tracking technologies such as inertial measurement units (IMUs), depth-sensor cameras, and markerless techniques ( Karatsidis et al, 2017 ; Ripic et al, 2022 ), to offer less-constrained application possibilities. Although promising, the error levels for the derived joint moments might still influence clinical decisions, and further improvements are required before the approach can be reliably translated into clinical settings.…”

Section: Discussionmentioning

confidence: 99%

Prediction of ground reaction forces and moments during walking in children with cerebral palsy

Kloeckner

Visscher

Taylor

et al. 2023

Front. Hum. Neurosci.

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IntroductionGait analysis is increasingly used to support clinical decision-making regarding diagnosis and treatment planning for movement disorders. As a key part of gait analysis, inverse dynamics can be applied to estimate internal loading conditions during movement, which is essential for understanding pathological gait patterns. The inverse dynamics calculation uses external kinetic information, normally collected using force plates. However, collection of external ground reaction forces (GRFs) and moments (GRMs) can be challenging, especially in subjects with movement disorders. In recent years, a musculoskeletal modeling-based approach has been developed to predict external kinetics from kinematic data, but its performance has not yet been evaluated for altered locomotor patterns such as toe-walking. Therefore, the goal of this study was to investigate how well this prediction method performs for gait in children with cerebral palsy.MethodsThe method was applied to 25 subjects with various forms of hemiplegic spastic locomotor patterns. Predicted GRFs and GRMs, in addition to associated joint kinetics derived using inverse dynamics, were statistically compared against those based on force plate measurements.ResultsThe results showed that the performance of the predictive method was similar for the affected and unaffected limbs, with Pearson correlation coefficients between predicted and measured GRFs of 0.71–0.96, similar to those previously reported for healthy adults, despite the motor pathology and the inclusion of toes-walkers within our cohort. However, errors were amplified when calculating the resulting joint moments to an extent that could influence clinical interpretation.ConclusionTo conclude, the musculoskeletal modeling-based approach for estimating external kinetics is promising for pathological gait, offering the possibility of estimating GRFs and GRMs without the need for force plate data. However, further development is needed before implementation within clinical settings becomes possible.

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Section: Discussionmentioning

confidence: 99%

Prediction of ground reaction forces and moments during walking in children with cerebral palsy

Kloeckner

Visscher

Taylor

et al. 2023

Front. Hum. Neurosci.

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“…Wearable accelerometers in combination with deep learning models [ 9 ] or depth cameras in combination with musculoskeletal models [ 10 ] might facilitate in-field prediction of multidimensional GRF. Wearable technologies to estimate vertical GRF in-field exist and use physics-based [ 11 ] and artificial intelligence (AI) approaches [ 12 , 13 , 14 ], although their ability to estimate GRF for different tasks and environments is yet to be verified.…”

Section: Introductionmentioning

confidence: 99%

Limiting the Use of Electromyography and Ground Reaction Force Data Changes the Magnitude and Ranking of Modelled Anterior Cruciate Ligament Forces

et al. 2023

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Neuromusculoskeletal models often require three-dimensional (3D) body motions, ground reaction forces (GRF), and electromyography (EMG) as input data. Acquiring these data in real-world settings is challenging, with barriers such as the cost of instruments, setup time, and operator skills to correctly acquire and interpret data. This study investigated the consequences of limiting EMG and GRF data on modelled anterior cruciate ligament (ACL) forces during a drop–land–jump task in late-/post-pubertal females. We compared ACL forces generated by a reference model (i.e., EMG-informed neural mode combined with 3D GRF) to those generated by an EMG-informed with only vertical GRF, static optimisation with 3D GRF, and static optimisation with only vertical GRF. Results indicated ACL force magnitude during landing (when ACL injury typically occurs) was significantly overestimated if only vertical GRF were used for either EMG-informed or static optimisation neural modes. If 3D GRF were used in combination with static optimisation, ACL force was marginally overestimated compared to the reference model. None of the alternative models maintained rank order of ACL loading magnitudes generated by the reference model. Finally, we observed substantial variability across the study sample in response to limiting EMG and GRF data, indicating need for methods incorporating subject-specific measures of muscle activation patterns and external loading when modelling ACL loading during dynamic motor tasks.

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“…[1][2][3] Its ability to perceive depth and track kinematic data has opened doors for low-cost and portable alternatives to otherwise expensive and elaborate methods. [4][5][6][7] Marker less motion analysis is one of such applications, wherein the Kinect has already been implemented for its potential characteristics. 3,8 The Kinect is equipped with an infrared light (IR) emitter and sensor, a colour camera and an array of microphones.…”

Section: Introductionmentioning

confidence: 99%

“…They provide two different perspectives on the scene much like human vision and triangulation is used to estimate the distance of each point from the Kinect, thus providing a stream of depth information. 7 This allows the Kinect to map out the scene and, more specifically, identify human shapes and its correspondent joints. As a result, a final skeleton can be used to recognize body postures and movements, ultimately used for motion analysis 5 and on physical therapies and rehabilitation monitoring process.…”

Section: Introductionmentioning

confidence: 99%

“…[9][10][11] The average noise levels of the obtained depth data are reported to be of similar magnitude with soft-tissue artifacts in motion capture systems such as Vicon, 12 indicating that the Kinect is a viable tool for motion analysis. However, occlusion of body parts, 7 poor IRreflectivity in clothing and relative subject movements outside the field of view 4 are reported to decrease the Kinect's accuracy. Furthermore, it is seemingly unable to correctly assess joint angles 13,14 small range of hand movements 15 and maintain a constant anthropometry per subject.…”

Section: Introductionmentioning

confidence: 99%

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Testing the Microsoft kinect skeletal tracking accuracy under varying external factors

Díaz¹,

Boss²,

Kyberd³

et al. 2022

MOJABB

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Focusing on its possible use in motion analysis, the accuracy of the Microsoft Kinect was investigated under various external factors including relative position, external IR light, computational power and large nearby surfaces. Two different experiments were performed that either focused on a general situation in an open room or when seated at a table. Results indicated that a large number of factors signiﬁcantly affect the measurement error, but with only minor effect sizes, where the relative position and orientation have shown to be most inﬂuential. Additionally, body movement and increased depth contrast (i.e. isolation from surrounding objects) are believed to increase the accuracy of the skeletal tracking process.

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Ground reaction force and joint moment estimation during gait using an Azure Kinect-driven musculoskeletal modeling approach

Cited by 19 publications

References 30 publications

Prediction of ground reaction forces and moments during walking in children with cerebral palsy

Prediction of ground reaction forces and moments during walking in children with cerebral palsy

Limiting the Use of Electromyography and Ground Reaction Force Data Changes the Magnitude and Ranking of Modelled Anterior Cruciate Ligament Forces

Testing the Microsoft kinect skeletal tracking accuracy under varying external factors

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