Ambulatory assessment of 3D ground reaction force using plantar pressure distribution

Rouhani, Hossein; Favre, Julien; Crevoisier, Xavier; Aminian, Kamiar

doi:10.1016/j.gaitpost.2010.05.014

Cited by 80 publications

(74 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is not evident that this also applies to the IFS, since the entire shoe is elevated (not only the heel). In another study, plantar pressure insoles have been used to estimate 3D GRF to avoid the influence of changed interface between the shoe and the ground [33]. In this approach, the limitations of an IFS in clinical applications can be further reduced.…”

Section: Kineticsmentioning

confidence: 99%

Influence of the instrumented force shoe on gait pattern in patients with osteoarthritis of the knee

Noort

Esch

Steultjens

et al. 2011

Med Biol Eng Comput

View full text Add to dashboard Cite

Osteoarthritis (OA) of the knee is associated with alterations in gait. As an alternative to force plates, instrumented force shoes (IFSs) can be used to measure ground reaction forces. This study evaluated the influence of IFS on gait pattern in patients with knee OA. Twenty patients with knee OA walked in a gait laboratory on IFS and control shoes (CSs). An optoelectronic system and force plate were used to perform 3D gait analyses. A comparison of temporalspatial gait parameters, kinematics, and kinetics was made between IFS and CS. Patients wearing IFS showed a decrease in walking velocity and cadence (8%), unchanged stride length, an increase in stance time (13%), stride time (11%) and step width (14%). No differences were found in knee adduction moment or knee kinematics. Small differences were found in foot and ankle kinematics (2-5°), knee transverse moments (5%), ankle frontal (3%) and sagittal moments (1%) and ground reaction force (1-6%). The gait of patients with knee OA was only mildly influenced by the IFS, due to increased shoe height and weight and a change in sole stiffness. The changes were small compared to normal variation and clinically relevant differences. Importantly, in OA patients no effect was found on the knee adduction moment.

show abstract

Section: Kineticsmentioning

confidence: 99%

Influence of the instrumented force shoe on gait pattern in patients with osteoarthritis of the knee

Noort

Esch

Steultjens

et al. 2011

Med Biol Eng Comput

View full text Add to dashboard Cite

show abstract

“…An overall RMS error of 5.4% and 6.4% was obtained for GRF estimation of the control subjects and stroke patients, respectively. Rouhani et al [121] compares GRF estimation using linear regression and nonlinear mapping functions -multi-layer perceptron (MLP) network and locally linear neuro-fuzzy (LLNF) model. Nonlinear mapping functions are shown to have lower Normalised RMS errors of 7.28N for MLP and 7.66N for LLNF in comparison to 10.69N for linear regression where stance time percentage is also used as an additional input.…”

Section: ) Pressure Insole Grf Estimationmentioning

confidence: 99%

Wearable Sensing for Solid Biomechanics

Wong

Zhang

et al. 2015

IEEE Sensors J.

View full text Add to dashboard Cite

Abstract-Understanding the solid biomechanics of the human body is important to the study of structure and function of the body, which can have a range of applications in healthcare, sport, wellbeing, and workflow analysis. Conventional laboratorybased biomechanical analysis systems and observation-based tests are only designed to capture brief snapshots of the mechanics of movement. With recent developments in wearable sensing technologies, biomechanical analysis can be conducted in less constrained environments, thus allowing continuous monitoring and analysis beyond laboratory settings. In this paper, we review the current research in wearable sensing technologies for biomechanical analysis, focusing upon sensing and analytics that enable continuous, long-term monitoring of kinematics and kinetics in a free-living environment. The main technical challenges, including measurement drift, external interferences, nonlinear sensor properties, sensor placement, and muscle variations that can affect the accuracy and robustness of existing methods, and different methods for reducing the impact of these sources of errors are described in this review. Recent developments in motion estimation in kinematics, mobile force sensing in kinematics, sensor reduction for electromyography, as well as the future direction of sensing for biomechanics are also discussed.

show abstract

“…But the use of ANNs in biomechanical analysis techniques still remains in its infancy [17]. Recently, studies have shown that ANNs may be fit tools for prediction in sports as well as biomechanics, for example the use of ANN to predict the hip, knee, and ankle sagittal moments during a vertical jump by using the output data from a force platform [18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

An Artificial Neural Network Approach for Predicting Kinematics in Handball Throws

Akl¹

2017

AJSS

View full text Add to dashboard Cite

Abstract:The purpose of this study was to test a new method to predict the kinematics of center of mass (COM) during the take-off phase of the handball shot by mean of multilayer perceptron neural networks (MLPs) based on data from only the force platform. Ten trials' of handball jump shot data from the force platform were obtained. The kinetic data of jump shot trials (force, impulse, and work) were used to feed the net and the data from the force platform kinematics (acceleration, velocity, and displacement) was used to evaluate the production data of the MLP neural network model. A commercial artificial neural network software was used to predict the target kinematic parameters (NeuroDimension, 2014®). The Pearson correlations of all Kinetics parameters between the original and production data was >0.99. The MLPs model successfully predicted the target kinematics depending on kinetics in the handball jump shot under the conditions of this study.

show abstract

Ambulatory assessment of 3D ground reaction force using plantar pressure distribution

Cited by 80 publications

References 21 publications

Influence of the instrumented force shoe on gait pattern in patients with osteoarthritis of the knee

Influence of the instrumented force shoe on gait pattern in patients with osteoarthritis of the knee

Wearable Sensing for Solid Biomechanics

An Artificial Neural Network Approach for Predicting Kinematics in Handball Throws

Contact Info

Product

Resources

About