Muscle Contracture Emulating System for Studying Artificially Induced Pathological Gait in Intact Individuals

2005 ◽  
Vol 21 (4) ◽  
pp. 348-358 ◽  
Author(s):  
Andrej Olenšek ◽  
Zlatko Matjačić ◽  
Tadej Bajd
Keyword(s):  
Mechanical System ◽  
Gait Pattern ◽  
Gait Variability ◽  
Spastic Diplegia ◽  
Gait Patterns ◽  
Gait Kinematics ◽  
Novel Method ◽  
Pathological Gait ◽  
Analysis System ◽  
Kinematics And Kinetics

When studying pathological gait it is important to correctly identify primary gait anomalies originating from damage to the central nervous and musculoskeletal system and separate them from compensatory changes of gait pattern, which is often challenging due to the lack of knowledge related to biomechanics of pathological gait. A mechanical system consisting of specially designed trousers, special shoe arrangement, and elastic ropes attached to selected locations on the trousers and shoes is proposed to allow emulation of muscle contractures of soleus (SOL) and gastrocnemius (GAS) muscles and both SOL-GAS. The main objective of this study was to evaluate and compare gait variability as recorded in normal gait and when being constrained with the proposed system. Six neurologically and orthopedically intact volunteers walked along a 7-m walkway while gait kinematics and kinetics were recorded using VICON motion analysis system and two AMTI forceplates. Statistical analysis of coefficient of variation of kinematics and kinetics as recorded in normal walking and during the most constrained SOL-GAS condition showed comparable gait variability. Inspection of resulting group averaged gait patterns revealed considerable resemblance to a selected clinical example of spastic diplegia, indicating that the proposed mechanical system potentially represents a novel method for studying emulated pathological gait arising from artificially induced muscle contractures in neurologically intact individuals.

scholarly journals An Advanced Attention-Enhanced Hybrid Deep Learning Model for WBSNs’ Gait Pattern Recognition

2022 ◽  
Author(s):  
Jianning Wu ◽  
Qiaoling Tan ◽  
Xiaoyan Wu
Keyword(s):  
Pattern Recognition ◽  
Deep Learning ◽  
Nonlinear Dynamic ◽  
Gait Pattern ◽  
Gait Variability ◽  
Gait Patterns ◽  
Generalization Performance ◽  
Dynamic Correlation ◽  
Gait Characteristics ◽  
Deep Learning Model

Abstract Background: The deep learning techniques have been attracted increasing attention on wireless body sensor networks (WBSNs) gait pattern recognition that has a great contribution to monitoring gait change in clinical application. However, in existing studies, there are some challenging issues such as low generalization performance and no potential interpretation for gait variability. It is necessary to search for the advanced deep learning models to resolve these issues. Method: A public WARD database including acceleration and gyroscope data acquired from each subject wearing five sensors was selected, and the gait with different combination of on-body multi-sensors is considered as a WBSNs’ gait pattern. An advanced attention-enhanced hybrid deep learning model of DCNN and LSTM for WBSNs’ gait pattern recognition was proposed. In our proposed technique, the combination model of DCNN with LSTM is firstly to discover the spatial-temporary gait correlation features. And then the attention mechanism is introduced to exploit the more valuable intrinsic nonlinear dynamic correlation gait characteristics associated with gait variability hidden in spatial-temporary gait space obtained. This significantly contributes to enhancing the generalization performance and taking insight on gait variability in a certain anatomical region. Results: The ten gait patterns are randomly selected from WARD database to evaluate the feasibility of our proposed method. Our experiments demonstrated the superior generalization ability of our method to some models such as CNN-LSTM, DCNN-LSTM. Our proposed model could classify ten gait patterns with the highest accuracy and F1-score of 91.48% and 91.46%, respectively. Moreover, we also found that the classification performance of a certain gait pattern was almost same best when the combinations of three or five on-body sensors were employed respectively, suggesting that our method possibly take insight on gait variability in a certain anatomical region. Conclusion: Our proposed technique could feasibly discover the more intrinsic nonlinear dynamic correlation gait characteristics associated with gait variability from on-body multi-sensors gait data, which greatly contributed to best generalization performance and potential clinical interpretation. Our proposed technique would hopefully become a powerful tool of monitoring gait change in clinical application.

Estimates of Gastrocnemius Muscle Length During Simulated Pathological Gait

2013 ◽  
Vol 29 (2) ◽  
pp. 127-134 ◽  
Author(s):  
Smita Rao ◽  
Fred Dietz ◽  
H. John Yack
Keyword(s):  
Gastrocnemius Muscle ◽  
Muscle Length ◽  
Gait Pattern ◽  
Line Model ◽  
Gait Patterns ◽  
Segmented Model ◽  
Normal Gait ◽  
Straight Line ◽  
Crouch Gait ◽  
Pathological Gait

The purpose of this study was to compare estimates of gastrocnemius muscle length (GML) obtained using a segmented versus straight-line model in children. Kinematic data were acquired on eleven typically developing children as they walked under the following conditions: normal gait, crouch gait, equinus gait, and crouch with equinus gait. Maximum and minimum GML, and GML change were calculated using two models: straight-line and segmented. A two-way RMANOVA was used to compare GML characteristics. Results indicated that maximum GML and GML change during simulated pathological gait patterns were influenced by model used to calculate gastrocnemius muscle length (interaction: P = .004 and P = .026). Maximum GML was lower in the simulated gait patterns compared with normal gait (P < .001). Maximum GML was higher with the segmented model compared with the straight-line model (P = .030). Using either model, GML change in equinus gait and crouch with equinus gait was lower compared with normal gait (P < .001). Overall, minimum GML estimated with the segmented model was higher compared with the straight-line model (P < .01). The key findings of our study indicate that GML is significantly affected by both gait pattern and method of estimation. The GML estimates tended to be lower with the straight-line model versus the segmented model.

Principal component models of knee kinematics and kinetics: Normal vs. pathological gait patterns

1997 ◽  
Vol 16 (2-3) ◽  
pp. 201-217 ◽  
Author(s):  
Kevin J. Deluzio ◽  
Urs P. Wyss ◽  
Benny Zee ◽  
Patrick A. Costigan ◽  
Charles Serbie
Keyword(s):  
Knee Kinematics ◽  
Principal Component ◽  
Gait Patterns ◽  
Pathological Gait ◽  
Component Models ◽  
Kinematics And Kinetics

scholarly journals User Identification from Gait Analysis Using Multi-Modal Sensors in Smart Insole

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3785 ◽  
Author(s):  
Sang-Il Choi ◽  
Jucheol Moon ◽  
Hee-Chan Park ◽  
Sang Tae Choi
Keyword(s):  
Time Series ◽  
Discriminant Analysis ◽  
Null Space ◽  
Gait Pattern ◽  
High Accuracy ◽  
Gait Patterns ◽  
Linear Discriminant ◽  
Acceleration Data ◽  
Novel Method ◽  
Smart Insole

Recent studies indicate that individuals can be identified by their gait pattern. A number of sensors including vision, acceleration, and pressure have been used to capture humans’ gait patterns, and a number of methods have been developed to recognize individuals from their gait pattern data. This study proposes a novel method of identifying individuals using null-space linear discriminant analysis on humans’ gait pattern data. The gait pattern data consists of time series pressure and acceleration data measured from multi-modal sensors in a smart insole used while walking. We compare the identification accuracies from three sensing modalities, which are acceleration, pressure, and both in combination. Experimental results show that the proposed multi-modal features identify 14 participants with high accuracy over 95% from their gait pattern data of walking.

scholarly journals The Importance of Being Earnest about Shank and Thigh Kinematics Especially When Using Ankle-Foot Orthoses

2010 ◽  
Vol 34 (3) ◽  
pp. 254-269 ◽  
Author(s):  
Elaine Owen
Keyword(s):  
Joint Kinematics ◽  
Foot Orthoses ◽  
Traditional Beliefs ◽  
Vertical Angle ◽  
Gait Biomechanics ◽  
Normal Gait ◽  
Ankle Foot Orthoses ◽  
Pathological Gait ◽  
Kinematics And Kinetics

This paper reviews and summarizes the evidence for important observations of normal and pathological gait and presents an approach to rehabilitation and orthotic management, which is based on the significance of shank and thigh kinematics for standing and gait. It discusses normal gait biomechanics, challenging some traditional beliefs, the interrelationship between segment kinematics, joint kinematics and kinetics and their relationship to orthotic design, alignment and tuning. It proposes a description of four rather than three rockers in gait; a simple categorization of pathological gait based on shank kinematics abnormality; an algorithm for the designing, aligning and tuning of AFO-Footwear Combinations; and an algorithm for determining the sagittal angle of the ankle in an AFO. It reports the results of research on Shank to Vertical Angle alignment of tuned AFO-Footwear Combinations and on the use of ‘point loading’ rocker soles.

scholarly journals Gait Variability and Complexity during Single and Dual-Task Walking on Different Surfaces in Outdoor Environment

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4792
Author(s):  
Denisa Nohelova ◽  
Lucia Bizovska ◽  
Nicolas Vuillerme ◽  
Zdenek Svoboda
Keyword(s):  
Dual Task ◽  
Inertial Sensor ◽  
Cognitive Task ◽  
Real Life ◽  
Gait Pattern ◽  
Gait Variability ◽  
Outdoor Environment ◽  
Manual Task ◽  
Lower Trunk ◽  
Trunk Acceleration

Nowadays, gait assessment in the real life environment is gaining more attention. Therefore, it is desirable to know how some factors, such as surfaces (natural, artificial) or dual-tasking, influence real life gait pattern. The aim of this study was to assess gait variability and gait complexity during single and dual-task walking on different surfaces in an outdoor environment. Twenty-nine healthy young adults aged 23.31 ± 2.26 years (18 females, 11 males) walked at their preferred walking speed on three different surfaces (asphalt, cobbles, grass) in single-task and in two dual-task conditions (manual task—carrying a cup filled with water, cognitive task—subtracting the number 7). A triaxial inertial sensor attached to the lower trunk was used to record trunk acceleration during gait. From 15 strides, sample entropy (SampEn) as an indicator of gait complexity and root mean square (RMS) as an indicator of gait variability were computed. The findings demonstrate that in an outdoor environment, the surfaces significantly impacted only gait variability, not complexity, and that the tasks affected both gait variability and complexity in young healthy adults.

The effects of walking sticks on gait kinematics and kinetics with chronic stroke survivors

2012 ◽  
Vol 27 (2) ◽  
pp. 131-137 ◽  
Author(s):  
Janaine Cunha Polese ◽  
Luci Fuscaldi Teixeira-Salmela ◽  
Lucas Rodrigues Nascimento ◽  
Christina Danielli Morais Faria ◽  
Renata Noce Kirkwood ◽  
...  
Keyword(s):  
Chronic Stroke ◽  
Stroke Survivors ◽  
Gait Kinematics ◽  
Kinematics And Kinetics

Dynamic and Reactive Walking for Humanoid Robots Based on Foot Placement Control

2016 ◽  
Vol 13 (02) ◽  
pp. 1550041 ◽  
Author(s):  
Juan Alejandro Castano ◽  
Zhibin Li ◽  
Chengxu Zhou ◽  
Nikos Tsagarakis ◽  
Darwin Caldwell
Keyword(s):  
Center Of Mass ◽  
Humanoid Robots ◽  
Gait Pattern ◽  
Gait Patterns ◽  
Foot Placement ◽  
Inverted Pendulum Model ◽  
Gait Parameters ◽  
Pendulum Model ◽  
Single Mass ◽  
Walking Velocity

This paper presents a novel online walking control that replans the gait pattern based on our proposed foot placement control using the actual center of mass (COM) state feedback. The analytic solution of foot placement is formulated based on the linear inverted pendulum model (LIPM) to recover the walking velocity and to reject external disturbances. The foot placement control predicts where and when to place the foothold in order to modulate the gait given the desired gait parameters. The zero moment point (ZMP) references and foot trajectories are replanned online according to the updated foothold prediction. Hence, only desired gait parameters are required instead of predefined or fixed gait patterns. Given the new ZMP references, the extended prediction self-adaptive control (EPSAC) approach to model predictive control (MPC) is used to minimize the ZMP response errors considering the acceleration constraints. Furthermore, to ensure smooth gait transitions, the conditions for the gait initiation and termination are also presented. The effectiveness of the presented gait control is validated by extensive disturbance rejection studies ranging from single mass simulation to a full body humanoid robot COMAN in a physics based simulator. The versatility is demonstrated by the control of reactive gaits as well as reactive stepping from standing posture. We present the data of the applied disturbances, the prediction of sagittal/lateral foot placements, the replanning of the foot/ZMP trajectories, and the COM responses.

scholarly journals Integrating Audio Signal Processing and Deep Learning Algorithms for Gait Pattern Classification in Brazilian Gaited Horses

2021 ◽  
Vol 2 ◽  
Author(s):  
Anderson Antonio Carvalho Alves ◽  
Lucas Tassoni Andrietta ◽  
Rafael Zinni Lopes ◽  
Fernando Oliveira Bussiman ◽  
Fabyano Fonseca e Silva ◽  
...  
Keyword(s):  
Neural Networks ◽  
Signal Processing ◽  
Deep Learning ◽  
Audio Signal ◽  
Gait Pattern ◽  
Classification Performance ◽  
Audio Signal Processing ◽  
Gait Patterns ◽  
Audio Features ◽  
Gaited Horses

This study focused on assessing the usefulness of using audio signal processing in the gaited horse industry. A total of 196 short-time audio files (4 s) were collected from video recordings of Brazilian gaited horses. These files were converted into waveform signals (196 samples by 80,000 columns) and divided into training (N = 164) and validation (N = 32) datasets. Twelve single-valued audio features were initially extracted to summarize the training data according to the gait patterns (Marcha Batida—MB and Marcha Picada—MP). After preliminary analyses, high-dimensional arrays of the Mel Frequency Cepstral Coefficients (MFCC), Onset Strength (OS), and Tempogram (TEMP) were extracted and used as input information in the classification algorithms. A principal component analysis (PCA) was performed using the 12 single-valued features set and each audio-feature dataset—AFD (MFCC, OS, and TEMP) for prior data visualization. Machine learning (random forest, RF; support vector machine, SVM) and deep learning (multilayer perceptron neural networks, MLP; convolution neural networks, CNN) algorithms were used to classify the gait types. A five-fold cross-validation scheme with 10 repetitions was employed for assessing the models' predictive performance. The classification performance across models and AFD was also validated with independent observations. The models and AFD were compared based on the classification accuracy (ACC), specificity (SPEC), sensitivity (SEN), and area under the curve (AUC). In the logistic regression analysis, five out of the 12 audio features extracted were significant (p &lt; 0.05) between the gait types. ACC averages ranged from 0.806 to 0.932 for MFCC, from 0.758 to 0.948 for OS and, from 0.936 to 0.968 for TEMP. Overall, the TEMP dataset provided the best classification accuracies for all models. The most suitable method for audio-based horse gait pattern classification was CNN. Both cross and independent validation schemes confirmed that high values of ACC, SPEC, SEN, and AUC are expected for yet-to-be-observed labels, except for MFCC-based models, in which clear overfitting was observed. Using audio-generated data for describing gait phenotypes in Brazilian horses is a promising approach, as the two gait patterns were correctly distinguished. The highest classification performance was achieved by combining CNN and the rhythmic-descriptive AFD.

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