Analysis and prediction of upper extremity movements by cyclograms

Kutílek, Patrik; Socha, Vladimír; Hána, Karel

doi:10.2478/s11536-013-0322-y

Cited by 8 publications

(7 citation statements)

References 22 publications

(22 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main disadvantages of the one-camera setup in use are: • Lower framerate (30fps vs 120+ fps from 3D cameras); • Tracking is done in the visible light spectrum, meaning that the lighting conditions can affect the tracking performance. A similar study was conducted by Kutilek et al (Kutilek and Farkasova, 2011;Kutilek and Viteckova, 2012). This study, however, has several advantages: the data acquisition system is much simpler and more accessible than the one used by the above-mentioned authors (single consumer-grade camera and 2D acquisition system with simple calibration, vs multi infrared-cameras with 3D acquisition); the predictive system is simpler and needs less information to perform the prediction, which reduces the neural network complexity and computing time, both desirable aspects for this kind of application.…”

Section: Discussionsupporting

confidence: 65%

“…An important characteristic identified by the author is that, for the analyzed variables, the ANN's predictive power using a simple setup was similar to its predictive power using more complex setups. When comparing this study with Kutilek's (Kutilek and Farkasova, 2011;Kutilek and Viteckova, 2012), these characteristics can also be identified for the analyzed data.…”

Section: Discussionmentioning

confidence: 65%

“…Sharma et al achieved the same results using genetic programming with multiple classifiers (Sharma et al, 2016). Kutilek et al developed a system based on artificial neural networks (ANNs), capable of predicting gait from a cyclogram (Kutilek and Farkasova, 2011;Kutilek and Viteckova, 2012), using information such as actual joint angles, angular acceleration, weight, and age of the individual, in addition to elements based on linear regression and principal component analysis (PCA).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Reconstruction of gait biomechanical parameters using cyclograms and artificial neural networks

Caparelli

Naves

2017

Res. Biomed. Eng.

View full text Add to dashboard Cite

Introduction: Historically, assessing the quality of human gait has been a difficult process. Advanced studies can be conducted using modern 3D systems. However, due to their high cost, usage of these 3D systems is still restricted to research environments. 2D systems offer simpler and more affordable solutions. Methods: In this study, the gait of 40 volunteers walking on a treadmill was recorded in the sagittal plane, using a 2D motion capture system. The extracted joint angles data were used to create cyclograms. Sections of the cyclograms were used as inputs to artificial neural networks (ANNs), since they can represent the kinematic behavior of the lower body. This allowed for prediction of future states of the moving body. Results: The results indicate that ANNs can predict the future states of the gait with high accuracy. Both single point and section predictions were successfully performed. Pearson's correlation coefficient and matched-pairs t-test ensured that the results were statistically significant. Conclusion: The combined use of ANNs and simple, accessible hardware is of great value in clinical practice. The use of cyclograms facilitates the analysis, as several gait characteristics can be easily recognized by their geometric shape. The predictive model presented in this paper facilitates generation of data that can be used in robotic locomotion therapy as a control signal or feedback element, aiding in the rehabilitation process of patients with motor dysfunction. The system proposes an interesting tool that can be explored to increase rehabilitation possibilities, providing better quality of life to patients.

show abstract

Section: Discussionsupporting

confidence: 65%

Section: Discussionmentioning

confidence: 65%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Reconstruction of gait biomechanical parameters using cyclograms and artificial neural networks

Caparelli

Naves

2017

Res. Biomed. Eng.

View full text Add to dashboard Cite

show abstract

“…New findings can attempt the assessment of relationships between pressures in various foot areas in time. This procedure is typical for kinematics and use cyclograms or angle-angle diagrams (Kutilek & Viteckova, 2012).…”

Section: Discussionmentioning

confidence: 99%

The influence of valgus heel position on foot loading in a child's gait

Martinásková¹,

Honzíková²,

Janura³

et al. 2012

Acta Gymnica

View full text Add to dashboard Cite

BACKGROUND: Flat foot is a typical clinical sign in childhood, expressed as valgus positioning of the heel during vertical foot loading. This may lead to medial deviation of the foot axis and cause overloading of some foot areas.OBJECTIVE: To determine the influence of valgus position of the heel (both bilateral and unilateral) on foot loading during gait.METHODS: An experimental group consisting of children with bilateral heel valgus deformity (16 children, age 5.3 ± 1.3 years) and children with unilateral heel valgus deformity (14 children, age 5.6 ± 1.6 years). The control group comprised of 14 children (age 4.5 ± 1.2 years). For measuring foot loading during gait, the Footscan (RSScan International, Olen, Belgium) pressure plate was used. Each subject went through 8 trials of gait measurement. From each trial, 8 foot areas were evaluated. Data processing with mean values for each subject was performed by non-parametric tests (Mann-Whitney and Wilcoxon tests, Spearman correlation) in the STATISTICA programme (StatSoft, Inc., Tulsa, OK, USA).RESULTS: Pressure peak and pressure impulse in the first metatarsal was greater for the bilateral valgus group (p < 0.01) in comparison to the control group. The group with unilateral valgus deformity showed greater pressure peak in the medial heel (p < 0.05), lower pressure peak in the third metatarsal (p < 0.01), lower pressure impulse in the fourth metatarsal (p < 0.05) and lower pressure peak (p < 0.05) and impulse (p < 0.01) in the fifth metatarsal on the limb with non-valgus position of heel when compared to the limb with valgus heel position.CONCLUSION: The results show that valgus positioning of the heel influences foot loading in children during gait. The findings of this study suggest the necessity of a complex solution to the problem of preventing further progression of pathological changes.

show abstract

“…This artificial intelligence has been applied to analyzed the gait by using the time phase cycles behavior [14][15][16][17][18][19][20]. Whereas the gait prediction has been studied by integrating the EMG signal into the neural network [21][22][23][24], using the cyclograms [25] or by the combination of EMG and cyclograms [24].…”

Section: Introductionmentioning

confidence: 99%

Knee angle prediction during stair ascending gait of trans-femoral amputee Neural networks application

Pinitlertsakun

Charoensuk

2012

The 5th 2012 Biomedical Engineering International Conference

View full text Add to dashboard Cite

For the Trans-femoral (TF) amputee, the biomechanics of stair gait can be analyzed in only the stair descending. The information of stair ascending gait can be evaluated for only the normal side. The study with TF amputee mostly referred to the stair ascending by leading with the normal side rather than the prosthetic side as there was none of prosthesis can be the leading side to climb up the stair so none of TF amputee can make the step over step climbing up the stair. Since there was none of prosthesis can be the leading side to climb up the stair, none of TF amputee can make the step over step climbing up the stair and hence none of the stair ascending gait prediction model has been studied in transfemoral amputee, this study is aiming to figure out the stair gait ascending pattern in transfemoral amputee which imitated by the healthy person. Then, the neural network model is constructed to predict the knee angle during stair ascending, in which purpose to integrate into the controller of the prosthetic knee component to assist the stair ascending gait

show abstract

Analysis and prediction of upper extremity movements by cyclograms

Cited by 8 publications

References 22 publications

Reconstruction of gait biomechanical parameters using cyclograms and artificial neural networks

Reconstruction of gait biomechanical parameters using cyclograms and artificial neural networks

The influence of valgus heel position on foot loading in a child's gait

Knee angle prediction during stair ascending gait of trans-femoral amputee Neural networks application

Contact Info

Product

Resources

About