Real-Time Vertical Ground Reaction Force Estimation in a Unified Simulation Framework Using Inertial Measurement Unit Sensors

Recinos, Elliot; Abella, John R.; Riyaz, Shayan; Demircan, Emel

doi:10.3390/robotics9040088

Cited by 9 publications

(13 citation statements)

References 19 publications

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“…In prior research conducted on estimating GRF in jump movements using IMUs and biomechanical models, 17 IMUs were utilized [40]. Additionally, methodologies employing machine learning require a substantial amount of experimental data for training purposes [10].…”

Section: Advantages Of the Proposed Approachmentioning

confidence: 99%

Estimation of Ground Reaction Forces during Sports Movements by Sensor Fusion from Inertial Measurement Units with 3D Forward Dynamics Model

Koshio,

Takahashi,

Haraguchi

et al. 2024

Preprint

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Rotational jumps are crucial techniques in sports competitions. Estimating ground reaction forces (GRFs), one of the components constituting jumps, through a biomechanical model-based approach enables analysis even in environments where force plates or machine learning training data cannot be utilized. In this study, rotational jump movements involving twists on land were measured using inertial measurement units (IMUs) and estimated GRFs and body loads using a 3D forward dynamics model. Our estimation method, based on forward dynamics and optimization calculations, generated and optimized body movements using cost functions defined by motion measurements and internal body loads. To reduce the influence of the dynamic acceleration in the optimization calculation, the 3D orientation using sensor fusion composed of acceleration and angular velocity data obtained from IMUs and an extended Kalman filter was estimated. As a result, by generating movements based on the cost function, it was possible to calculate biomechanically valid GRFs while following the measured movements even if not all joints are covered by IMUs. This estimation method allows for 3D motion analysis independent of measurement conditions or training data.

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Section: Advantages Of the Proposed Approachmentioning

confidence: 99%

Estimation of Ground Reaction Forces during Sports Movements by Sensor Fusion from Inertial Measurement Units with 3D Forward Dynamics Model

Koshio,

Takahashi,

Haraguchi

et al. 2024

Preprint

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“…the second highest number of on-body sensors was 3, and they were found in 12 articles [29,34,36,38,47,48,57,59,64,65,85,92]. Full-body sensors, which consisted of 17 sensors, were seen in 6 articles [50,71,76,78,79,82]. Most of the studies (38 articles) measured movement with a sampling frequency in the range of 100-200 Hz [14,15,18,23,29,31,32,35,36,40,41,44,46,48,[52][53][54]56,57,59,64,66,[69][70][71][72]74,82,[85][86][87][88]…”

Section: Measurement Systems and Sensor Placementmentioning

confidence: 99%

“…Full-body sensors, which consisted of 17 sensors, were seen in 6 articles [50,71,76,78,79,82]. Most of the studies (38 articles) measured movement with a sampling frequency in the range of 100-200 Hz [14,15,18,23,29,31,32,35,36,40,41,44,46,48,[52][53][54]56,57,59,64,66,[69][70][71][72]74,82,[85][86][87][88][89][90][91][92][93]. The lowest sampling rate was found to be 40 Hz [39].…”

Section: Measurement Systems and Sensor Placementmentioning

confidence: 99%

Estimation of Kinetics Using IMUs to Monitor and Aid in Clinical Decision-Making during ACL Rehabilitation: A Systematic Review

Krishnakumar,

van Beijnum,

Baten

et al. 2024

Sensors

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After an ACL injury, rehabilitation consists of multiple phases, and progress between these phases is guided by subjective visual assessments of activities such as running, hopping, jump landing, etc. Estimation of objective kinetic measures like knee joint moments and GRF during assessment can help physiotherapists gain insights on knee loading and tailor rehabilitation protocols. Conventional methods deployed to estimate kinetics require complex, expensive systems and are limited to laboratory settings. Alternatively, multiple algorithms have been proposed in the literature to estimate kinetics from kinematics measured using only IMUs. However, the knowledge about their accuracy and generalizability for patient populations is still limited. Therefore, this article aims to identify the available algorithms for the estimation of kinetic parameters using kinematics measured only from IMUs and to evaluate their applicability in ACL rehabilitation through a comprehensive systematic review. The papers identified through the search were categorized based on the modelling techniques and kinetic parameters of interest, and subsequently compared based on the accuracies achieved and applicability for ACL patients during rehabilitation. IMUs have exhibited potential in estimating kinetic parameters with good accuracy, particularly for sagittal movements in healthy cohorts. However, several shortcomings were identified and future directions for improvement have been proposed, including extension of proposed algorithms to accommodate multiplanar movements and validation of the proposed techniques in diverse patient populations and in particular the ACL population.

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“…Among these approaches, the biomechanical simulation-based method has gained increasing attention from researchers and has seen successful applications in other research domains. Recinos et al utilized an integrated framework of Opensim and Unity to estimate ground reaction forces in a simulation environment using IMU data collected from actual motion experiments [26]. Jiang et al used fatigue detection motion experiment data from a limited number of participants to drive model movements in Opensim.…”

Section: Introductionmentioning

confidence: 99%

Synthetic IMU Datasets and Protocols Can Simplify Fall Detection Experiments and Optimize Sensor Configuration

Tang,

He,

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Falls represent a significant cause of injury among the elderly population. Extensive research has been devoted to the utilization of wearable IMU sensors in conjunction with machine learning techniques for fall detection. To address the challenge of acquiring costly training data, this paper presents a novel method that generates a substantial volume of synthetic IMU data with minimal actual fall experiments. First, unmarked 3D motion capture technology is employed to reconstruct human movements. Subsequently, utilizing the biomechanical simulation platform Opensim and forward kinematic methods, an ample amount of training data from various body segments can be custom generated. Synthetic IMU data was then used to train a machine learning model, achieving testing accuracies of 91.99% and 86.62% on two distinct datasets of actual fall-related IMU data. Building upon the simulation framework, this paper further optimized the single IMU attachment position and multiple IMU combinations on fall detection. The proposed method simplifies fall detection data acquisition experiments, provides novel venue for generating low cost synthetic data in scenario where acquiring data for machine learning is challenging and paves the way for customizing machine learning configurations.

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Real-Time Vertical Ground Reaction Force Estimation in a Unified Simulation Framework Using Inertial Measurement Unit Sensors

Cited by 9 publications

References 19 publications

Estimation of Ground Reaction Forces during Sports Movements by Sensor Fusion from Inertial Measurement Units with 3D Forward Dynamics Model

Estimation of Ground Reaction Forces during Sports Movements by Sensor Fusion from Inertial Measurement Units with 3D Forward Dynamics Model

Estimation of Kinetics Using IMUs to Monitor and Aid in Clinical Decision-Making during ACL Rehabilitation: A Systematic Review

Synthetic IMU Datasets and Protocols Can Simplify Fall Detection Experiments and Optimize Sensor Configuration

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