Estimation of Joint Forces and Moments for the In-Run and Take-Off in Ski Jumping Based on Measurements with Wearable Inertial Sensors

Logar, Grega; Munih, Marko

doi:10.3390/s150511258

Cited by 37 publications

(43 citation statements)

References 17 publications

(25 reference statements)

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“…The method implemented for detecting the time points for take-off t TO and touch-down t TD is introduced in this section: Due to the change of the ground reaction forces, both take-off and touch-down phase show abrupt changes in acceleration, which is measured by the accelerometer. Previous studies on ski jumping take-off [25] and landing [26,27] also presented similar acceleration changing behavior in their measurement-based results. Therefore, we propose to determine t TO and t TD by detecting these abrupt changes in the accelerometer measurement.…”

Section: Fundingsupporting

confidence: 60%

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Ski Jumping Trajectory Reconstruction Using Wearable Sensors via Extended Rauch-Tung-Striebel Smoother with State Constraints

Fang

Grüter

Piprek

et al. 2020

Sensors

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To satisfy an increasing demand to reconstruct an athlete’s motion for performance analysis, this paper proposes a new method for reconstructing the position and velocity in the context of ski jumping trajectories. Therefore, state-of-the-art wearable sensors, including an inertial measurement unit, a magnetometer, and a GPS logger are used. The method employs an extended Rauch-Tung-Striebel smoother with state constraints to estimate state information offline from recorded raw measurements. In comparison to the classic inertial navigation system and GPS integration solution, the proposed method includes additional geometric shape information of the ski jumping hill, which are modeled as soft constraints and embedded into the estimation framework to improve the position and velocity estimation accuracy. Results for both simulated measurement data and real measurement data demonstrate the effectiveness of the proposed method. Moreover, a comparison between jump lengths obtained from the proposed method and video recordings shows the relative root-mean-square error of the reconstructed jump length is below 1.5 m depicting the accuracy of the algorithm.

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Section: Fundingsupporting

confidence: 60%

“…, and v a = v k v c,k . By Equations 22, (25), and 26, we formulated the state-space presentation of an augmented system for state and parameter estimation with state constraints in the form of Equations (1)-(3).…”

Section: Constrained Filteringmentioning

confidence: 99%

Ski Jumping Trajectory Reconstruction Using Wearable Sensors via Extended Rauch-Tung-Striebel Smoother with State Constraints

Fang

Grüter

Piprek

et al. 2020

Sensors

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“…In a recent study, a top-down inverse dynamics approach was applied to estimate GRF&M and L5/S1 joint moments during trunk bending [ 24 ]. Another study used IMUs to estimate the joint forces and moments during ski jumping [ 25 ]. The common limitation of those studies is that they examined only the total external loads applied on both feet and are, therefore, inapplicable to gait analysis.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Ground Reaction Forces and Moments During Gait Using Only Inertial Motion Capture

Karatsidis

Bellusci

Schepers

et al. 2016

Sensors

208

227

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Ground reaction forces and moments (GRF&M) are important measures used as input in biomechanical analysis to estimate joint kinetics, which often are used to infer information for many musculoskeletal diseases. Their assessment is conventionally achieved using laboratory-based equipment that cannot be applied in daily life monitoring. In this study, we propose a method to predict GRF&M during walking, using exclusively kinematic information from fully-ambulatory inertial motion capture (IMC). From the equations of motion, we derive the total external forces and moments. Then, we solve the indeterminacy problem during double stance using a distribution algorithm based on a smooth transition assumption. The agreement between the IMC-predicted and reference GRF&M was categorized over normal walking speed as excellent for the vertical (ρ = 0.992, rRMSE = 5.3%), anterior (ρ = 0.965, rRMSE = 9.4%) and sagittal (ρ = 0.933, rRMSE = 12.4%) GRF&M components and as strong for the lateral (ρ = 0.862, rRMSE = 13.1%), frontal (ρ = 0.710, rRMSE = 29.6%), and transverse GRF&M (ρ = 0.826, rRMSE = 18.2%). Sensitivity analysis was performed on the effect of the cut-off frequency used in the filtering of the input kinematics, as well as the threshold velocities for the gait event detection algorithm. This study was the first to use only inertial motion capture to estimate 3D GRF&M during gait, providing comparable accuracy with optical motion capture prediction. This approach enables applications that require estimation of the kinetics during walking outside the gait laboratory.

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“…Chardonnens et al [1] built an IMU-based system to successfully measure the three-dimensional kinematics for ski jumpers. Logor et al [2] attached 10 IMUs on different body segments and measured the ski jumpers' movement. Moreover, they also calculated forces and moments acting on the joints.…”

Section: Introductionmentioning

confidence: 99%

Attitude Estimation of Skis in Ski Jumping Using Low-Cost Inertial Measurement Units

Fang¹,

Göttlicher²,

Holzapfel³

2018

The 12th Conference of the International Sports Engineering Association

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This paper presents an approach to estimate the attitude of skis for an entire ski jump using wearable, MEMS-based, low-cost Inertial Measurement Units (IMUs). First of all, a kinematic attitude model based on rigid-body dynamics and a sensor error model considering bias and scale factor error are established. Then, an extended Rauch-Tung-Striebel (RTS) smoother is used to combine measurement data provided by both gyroscope and magnetometer to achieve an attitude estimation. Moreover, parameters for the bias and scale factor error in the sensor error model and the initial attitude are determined via a maximum-likelihood principle based parameter estimation algorithm. By implementing this approach, an attitude estimation of skis is achieved without further sensor calibration. Finally, results based on both the simulated reference data and the real experimental measurement data are presented, which proves the practicability and the validity of the proposed approach.

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Estimation of Joint Forces and Moments for the In-Run and Take-Off in Ski Jumping Based on Measurements with Wearable Inertial Sensors

Cited by 37 publications

References 17 publications

Ski Jumping Trajectory Reconstruction Using Wearable Sensors via Extended Rauch-Tung-Striebel Smoother with State Constraints

Ski Jumping Trajectory Reconstruction Using Wearable Sensors via Extended Rauch-Tung-Striebel Smoother with State Constraints

Estimation of Ground Reaction Forces and Moments During Gait Using Only Inertial Motion Capture

Attitude Estimation of Skis in Ski Jumping Using Low-Cost Inertial Measurement Units

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