THE INFLUENCE OF FILTER PARAMETERS ON THE PREDICTION ACCURACY OF THE GROUND REACTION FORCE AND JOINT MOMENTS

Sina David; Marion Mundt; Arnd Koeppe; Franz Bamer; Bernd Markert; Wolfgang Potthast

THE INFLUENCE OF FILTER PARAMETERS ON THE PREDICTION ACCURACY OF THE GROUND REACTION FORCE AND JOINT MOMENTS

Sina David, Marion Mundt, Arnd Koeppe, Franz Bamer, Bernd Markert, Wolfgang Potthast

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic

Abstract

Athletes’ movement biomechanics are of high interest to predict injury risk. However, using a standard optical measurement set-up with cameras and force plates influences the athlete’s performance. Alternative systems such as commercial IMU systems are still jeopardised by measurement discrepancies in the analysis of joint angles. Therefore, this study aims to estimate hip, knee and ankle joint angles from simulated IMU data during the execution and depart contact of a maximum effort 90° cutting manoeuvre using a feed-forward neural network. Simulated accelerations and angular rates of the feet, shanks, thighs and pelvis as input data. The correlation coefficient between the measured and predicted data indicates strong correlations. Hence, the proposed method can be used to predict motion kinematics during a fast change of direction.

Original language	Undefined/Unknown
Title of host publication	ISBS - Conference Proceedings Archive
Publisher	International Society of Biomechanics in Sports
Pages	427-430
Publication status	Published - 2019
Externally published	Yes

Bibliographical note

['Conference code: 37']

Persistent URL (handle)

Persistent link

Cite this

@inproceedings{3a57e2b759e34e7d8f80ffe341965a69,

title = "THE INFLUENCE OF FILTER PARAMETERS ON THE PREDICTION ACCURACY OF THE GROUND REACTION FORCE AND JOINT MOMENTS",

abstract = "Athletes{\textquoteright} movement biomechanics are of high interest to predict injury risk. However, using a standard optical measurement set-up with cameras and force plates influences the athlete{\textquoteright}s performance. Alternative systems such as commercial IMU systems are still jeopardised by measurement discrepancies in the analysis of joint angles. Therefore, this study aims to estimate hip, knee and ankle joint angles from simulated IMU data during the execution and depart contact of a maximum effort 90° cutting manoeuvre using a feed-forward neural network. Simulated accelerations and angular rates of the feet, shanks, thighs and pelvis as input data. The correlation coefficient between the measured and predicted data indicates strong correlations. Hence, the proposed method can be used to predict motion kinematics during a fast change of direction.",

author = "Sina David and Marion Mundt and Arnd Koeppe and Franz Bamer and Bernd Markert and Wolfgang Potthast",

note = "['Conference code: 37']",

year = "2019",

language = "Undefined/Unknown",

pages = "427--430",

booktitle = "ISBS - Conference Proceedings Archive",

publisher = "International Society of Biomechanics in Sports",

}

THE INFLUENCE OF FILTER PARAMETERS ON THE PREDICTION ACCURACY OF THE GROUND REACTION FORCE AND JOINT MOMENTS. / David, Sina; Mundt, Marion; Koeppe, Arnd et al.
ISBS - Conference Proceedings Archive. International Society of Biomechanics in Sports, 2019. p. 427-430.

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic

TY - GEN

T1 - THE INFLUENCE OF FILTER PARAMETERS ON THE PREDICTION ACCURACY OF THE GROUND REACTION FORCE AND JOINT MOMENTS

AU - David, Sina

AU - Mundt, Marion

AU - Koeppe, Arnd

AU - Bamer, Franz

AU - Markert, Bernd

AU - Potthast, Wolfgang

N1 - ['Conference code: 37']

PY - 2019

Y1 - 2019

N2 - Athletes’ movement biomechanics are of high interest to predict injury risk. However, using a standard optical measurement set-up with cameras and force plates influences the athlete’s performance. Alternative systems such as commercial IMU systems are still jeopardised by measurement discrepancies in the analysis of joint angles. Therefore, this study aims to estimate hip, knee and ankle joint angles from simulated IMU data during the execution and depart contact of a maximum effort 90° cutting manoeuvre using a feed-forward neural network. Simulated accelerations and angular rates of the feet, shanks, thighs and pelvis as input data. The correlation coefficient between the measured and predicted data indicates strong correlations. Hence, the proposed method can be used to predict motion kinematics during a fast change of direction.

AB - Athletes’ movement biomechanics are of high interest to predict injury risk. However, using a standard optical measurement set-up with cameras and force plates influences the athlete’s performance. Alternative systems such as commercial IMU systems are still jeopardised by measurement discrepancies in the analysis of joint angles. Therefore, this study aims to estimate hip, knee and ankle joint angles from simulated IMU data during the execution and depart contact of a maximum effort 90° cutting manoeuvre using a feed-forward neural network. Simulated accelerations and angular rates of the feet, shanks, thighs and pelvis as input data. The correlation coefficient between the measured and predicted data indicates strong correlations. Hence, the proposed method can be used to predict motion kinematics during a fast change of direction.

M3 - Conference contribution

SP - 427

EP - 430

BT - ISBS - Conference Proceedings Archive

PB - International Society of Biomechanics in Sports

ER -