Inverse dynamics calculations during gait with restricted ground reaction force information from pressure insoles

Forner-Cordero, Arturo; Koopman, H.J.F.M.; Helm, F.C.T. van der

doi:10.1016/j.gaitpost.2005.02.002

Cited by 84 publications

(51 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A fair agreement of the recently reported results was due to an improved modelling of global muscle torques generated in lower extremity joints during gait [12][13][14]. It may thus be assumed that the curves presented in Fig.…”

Section: Rigid Elementssupporting

confidence: 83%

Inverse Dynamics and Artificial Neural Network Applications in Gait Analysis of the Disabled Subjects

Wit¹,

Czaplicki²

2008

Human Movement

View full text Add to dashboard Cite

The aim of this study is to review the achievements of the mathematical modeling of muscles force contribution during walking. In order to determine the contributions of individual muscles to the net force or net muscle torque at a given joint, the external forces acting on lower extremity joints during gait ought to be identified. The solution of this problem, called in biomechanics the inverse dynamics, is now regarded as a classical method of movement modelling. In the hypothesis put forward in this paper, it is considered if the artificial neural network method could be applied to the muscle contraction prediction during gait analysis in normal and disabled subjects. Artificial neural network (ANN) is an artificial intelligence method used in mathematical modelling and its applications in diverse areas, especially in biology and medicine, are steadily progressing. The achievements and possibilities of ANN in biomechanics were presented previously by others authors. For example, Liu and Lockhart [13] attempted at creating a network capable of reproducing muscle forces during gait from EMG signals recorded in working muscles. The objective of our study was to make use of the experience gained in the construction of ANN and to apply advanced mathematical procedures to identical experimental conditions of gaint analysis.

show abstract

Section: Rigid Elementssupporting

confidence: 83%

Inverse Dynamics and Artificial Neural Network Applications in Gait Analysis of the Disabled Subjects

Wit¹,

Czaplicki²

2008

Human Movement

View full text Add to dashboard Cite

show abstract

“…Other research has examined force sensors inserted in the shoe. These devices have produced valid data when testing GRF during various types of gait [18,19]. Another approach to assessing GRF during gait successfully used force sensors embedded under the running surface of a treadmill [20].…”

Section: Discussionmentioning

confidence: 99%

Validation of a motion capture system for deriving accurate ground reaction forces without a force plate

et al. 2016

View full text Add to dashboard Cite

Background: Human movement such as physical work, exercise and sport activities can be analyzed to determine kinetic (force) and kinematic (motion) characteristics. In the past, proper assessment of force variables requires force plates that can limit the types of activities performed. Additionally, large amounts of data are often generated, requiring often tedious and time-consuming analyses and calculations. Therefore, the purpose of this project was to compare the vertical ground reaction forces obtained from a force plate with calculated GRFs derived from a motion capture system during body weight squats for five healthy men. Results: After performing three body weight squats, vertical ground reaction forces in Newtons were calculated for each data point for the force plate and motion capture system data, and synchronized from the point of peak force. There were no significant differences (p < .05) for peak force, lowest force, or mean force between trials or modalities (force plate or motion capture system). Comparison of force-time curves with regression analyses indicated excellent agreement between testing modalities (r = 0.995, r 2 = 0.989, SEM = 11.1 N).

show abstract

“…In addition, researchers in the biomechanical and rehabilitation fields have made use of modified insoles to sense pressure [9], [8], [10], [4]. Whilst relevant, these systems have tended to concentrate on accurate force measurements using expensive sensors and capture equipment that, whilst portable, is too bulky for sports people (intended instead for gait analysis).…”

Section: Rmsmentioning

confidence: 99%

Profiling sprints using on-body sensors

Taherian

Pias

Harle

et al. 2010

2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops)

View full text Add to dashboard Cite

Abstract-This paper describes the design, implementation and deployment of a wireless sensor system for athletes. The system is designed to profile sprints based on input from on-body sensors that are wirelessly connected to a nearby infrastructure. We discuss the choice and use of inexpensive Force Sensitive Resistors (FSRs) to measure foot event timings and provide a detailed analysis of the profiling method used to represent high-level information to the coaches and athletes.In this profiling method, we detect sprinting intervals from high-resolution sensor data, and compute the ground contact times for sprinting performances. We validate our results using force plates and show that the system achieves comparable accuracy in measuring the foot contact times (millisecond accuracy) without the limitations of one or few steps.

show abstract

Inverse dynamics calculations during gait with restricted ground reaction force information from pressure insoles

Cited by 84 publications

References 18 publications

Inverse Dynamics and Artificial Neural Network Applications in Gait Analysis of the Disabled Subjects

Inverse Dynamics and Artificial Neural Network Applications in Gait Analysis of the Disabled Subjects

Validation of a motion capture system for deriving accurate ground reaction forces without a force plate

Profiling sprints using on-body sensors

Contact Info

Product

Resources

About