Experimentelle Untersuchungen zur Reproduzierbarkeit von Ensemble-gemittelten elektromyographischen Ganganalysedaten im Bereich der experimentellen und klinischen Orthopädie / Experimental Investigation on the Reproducibility of Ensemble Averaged Electromyographic Gait Analysis Data in Experimental and Clinical Orthopaedics

2004 ◽  
Vol 49 (6) ◽  
pp. 170-176
Author(s):  
A Meurer ◽  
F Bodem ◽  
J Decking ◽  
G Lenhard ◽  
J Heine
Keyword(s):  
Experimental Investigation ◽  
Gait Analysis ◽  
Analysis Data ◽  
Experimentelle Untersuchungen

scholarly journals Deep Convolutional and LSTM Networks on Multi-Channel Time Series Data for Gait Phase Recognition

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 789
Author(s):  
David Kreuzer ◽  
Michael Munz
Keyword(s):  
Gait Analysis ◽  
Time Series Data ◽  
Low Cost ◽  
Analysis Data ◽  
Gait Disorders ◽  
Human Gait ◽  
Series Data ◽  
Ageing Society ◽  
Time Motion ◽  
Gait Phase

With an ageing society comes the increased prevalence of gait disorders. The restriction of mobility leads to a considerable reduction in the quality of life, because associated falls increase morbidity and mortality. Consideration of gait analysis data often alters surgical recommendations. For that reason, the early and systematic diagnostic treatment of gait disorders can spare a lot of suffering. As modern gait analysis systems are, in most cases, still very costly, many patients are not privileged enough to have access to comparable therapies. Low-cost systems such as inertial measurement units (IMUs) still pose major challenges, but offer possibilities for automatic real-time motion analysis. In this paper, we present a new approach to reliably detect human gait phases, using IMUs and machine learning methods. This approach should form the foundation of a new medical device to be used for gait analysis. A model is presented combining deep 2D-convolutional and LSTM networks to perform a classification task; it predicts the current gait phase with an accuracy of over 92% on an unseen subject, differentiating between five different phases. In the course of the paper, different approaches to optimize the performance of the model are presented and evaluated.

scholarly journals Agreement of Gait Events Detection during Treadmill Backward Walking by Kinematic Data and Inertial Motion Units

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6331
Author(s):  
Uri Gottlieb ◽  
Tharani Balasukumaran ◽  
Jay R. Hoffman ◽  
Shmuel Springer
Keyword(s):  
Gait Analysis ◽  
Balance Control ◽  
Analysis Data ◽  
Inertial Motion ◽  
Unique Challenge ◽  
Backward Walking ◽  
Orthopedic Rehabilitation ◽  
Optical Motion ◽  
Optical Motion Capture ◽  
Events Detection

Backward walking (BW) is being increasingly used in neurologic and orthopedic rehabilitation as well as in sports to promote balance control as it provides a unique challenge to the sensorimotor control system. The identification of initial foot contact (IC) and terminal foot contact (TC) events is crucial for gait analysis. Data of optical motion capture (OMC) kinematics and inertial motion units (IMUs) are commonly used to detect gait events during forward walking (FW). However, the agreement between such methods during BW has not been investigated. In this study, the OMC kinematics and inertial data of 10 healthy young adults were recorded during BW and FW on a treadmill at different speeds. Gait events were measured using both kinematics and inertial data and then evaluated for agreement. Excellent reliability (Interclass Correlation > 0.9) was achieved for the identification of both IC and TC. The absolute differences between methods during BW were 18.5 ± 18.3 and 20.4 ± 15.2 ms for IC and TC, respectively, compared to 9.1 ± 9.6 and 10.0 ± 14.9 for IC and TC, respectively, during FW. The high levels of agreement between methods indicate that both may be used for some applications of BW gait analysis.

Combined analysis of fMRI and gait analysis data for evaluating biofeedback rehabilitation in post-stroke patients

2011 ◽  
Vol 33 ◽  
pp. S10-S11
Author(s):  
S. Del Din ◽  
A. Bertoldo ◽  
Z. Sawacha ◽  
M. Rabuffetti ◽  
M. Laganà ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Analysis Data ◽  
Stroke Patients ◽  
Combined Analysis ◽  
Post Stroke

Intra- and inter-laboratory repeatability of gait analysis data in normal adults

2009 ◽  
Vol 30 ◽  
pp. S14
Author(s):  
Joseph Bevins ◽  
Sarah Churchill ◽  
Mark Corbett ◽  
Ralph Palmer ◽  
David Pratt ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Analysis Data ◽  
Normal Adults

Variability in Gait Analysis Data in Spastic Children

2005 ◽  
Vol 8 (3) ◽  
pp. 189-190
Author(s):  
Y. J. M. Janssen-Potten ◽  
M. L. P. Coenen ◽  
E. A. A. Rameckers ◽  
L. A. W. M. Speth ◽  
J. S. H. Vles
Keyword(s):  
Gait Analysis ◽  
Analysis Data
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