scholarly journals Locomotion of Ants Walking up Slippery Slopes of Granular Materials

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
A Humeau ◽  
M Piñeirua ◽  
J Crassous ◽  
J Casas
Keyword(s):  
Material Properties ◽  
Granular Media ◽  
Gait Pattern ◽  
Gait Patterns ◽  
Solid Media ◽  
Manipulative Experiments ◽  
Particle Level ◽  
Abnormality Index ◽  
Discrete Nature ◽  
Leg Kinematics

Abstract Many insects encounter locomotory difficulties in walking up sand inclines. This is masterfully exploited by some species for building traps from which prey are rarely able to escape, as the antlion and its deadly pit. The aim of this work is to tear apart the relative roles of granular material properties and slope steepness on the insect leg kinematics, gait patterns, and locomotory stability. For this, we used factorial manipulative experiments with different granular media inclines and the ant Aphaenogaster subterranea. Our results show that its locomotion is similar on granular and solid media, while for granular inclined slopes we observe a loss of stability followed by a gait pattern transition from tripod to metachronal. This implies that neither the discrete nature nor the roughness properties of sand alone are sufficient to explain the struggling of ants on sandy slopes: the interaction between sand properties and slope is key. We define an abnormality index that allows us to quantify the locomotory difficulties of insects walking up a granular incline. The probability of its occurrence reveals the local slipping of the granular media as a consequence of the pressure exerted by the ant’s legs. Our findings can be extended to other models presenting locomotory difficulties for insects, such as slippery walls of urns of pitcher plants. How small arthropods walking on granular and brittle materials solve their unique stability trade-off will require a thorough understanding of the transfer of energy from leg to substrate at the particle level.

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 < 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.

Texting While Walking Induces Gait Pattern Alterations in Healthy Older Adults

2018 ◽  
Vol 62 (1) ◽  
pp. 1908-1912 ◽  
Author(s):  
Massimiliano Pau ◽  
Micaela Porta ◽  
Giuseppina Pilloni ◽  
Federica Corona ◽  
Maria Chiara Fastame ◽  
...  
Keyword(s):  
Older Adults ◽  
Cognitive Complexity ◽  
Gait Pattern ◽  
Middle Aged ◽  
Similar Reduction ◽  
Gait Patterns ◽  
Healthy Older Adults ◽  
Double Support ◽  
Temporal Parameters ◽  
Spatio Temporal

The use of a mobile phone for texting purposes results in distracted walking which may lead to injuries. In particular, texting while walking has been shown to induce significant alterations in gait patterns. This study aimed to assess whether changes in the main spatio-temporal parameters of gait when simultaneously engaged in texting on a smartphone and walking are different in older adults relative to young and middle- aged individuals. A total of 57 participants divided in three groups (19 older adults aged over 65, 19 young aged 20-40 and 19 middle-aged aged 41-64) were tested in two conditions: walking, and walking while texting on a smartphone. Spatio-temporal parameters of gait were assessed using a wearable accelerometer located on the lower back. The results show that texting induced similar reduction of gait speed, stride length and cadence in all groups. Slight (although significant) alterations of stance, swing and double support phases duration were found only for middle-aged participants. Such findings suggest that modifications of gait patterns due to texting seem unaffected by age, probably due to different perceptions of the cognitive complexity of the task and differential prioritization of its motor and cognitive aspects.

scholarly journals An Unsupervised Data-Driven Model to Classify Gait Patterns in Children with Cerebral Palsy

2020 ◽  
Vol 9 (5) ◽  
pp. 1432
Author(s):  
Julie Choisne ◽  
Nicolas Fourrier ◽  
Geoffrey Handsfield ◽  
Nada Signal ◽  
Denise Taylor ◽  
...  
Keyword(s):  
Cerebral Palsy ◽  
Gait Pattern ◽  
Data Driven ◽  
Self Organizing Map ◽  
Gait Patterns ◽  
Gait Biomechanics ◽  
Children With Cerebral Palsy ◽  
Profile Score ◽  
Data Driven Modeling ◽  
Gait Variable

Ankle and foot orthoses are commonly prescribed to children with cerebral palsy (CP). It is unclear whether 3D gait analysis (3DGA) provides sufficient and reliable information for clinicians to be consistent when prescribing orthoses. Data-driven modeling can probe such questions by revealing non-intuitive relationships between variables such as 3DGA parameters and gait outcomes of orthoses use. The purpose of this study was to (1) develop a data-driven model to classify children with CP according to their gait biomechanics and (2) identify relationships between orthotics types and gait patterns. 3DGA data were acquired from walking trials of 25 typically developed children and 98 children with CP with additional prescribed orthoses. An unsupervised self-organizing map followed by k-means clustering was developed to group different gait patterns based on children’s 3DGA. Model inputs were gait variable scores (GVSs) extracted from the gait profile score, measuring root mean square differences from TD children’s gait cycle. The model identified five pathological gait patterns with statistical differences in GVSs. Only 43% of children improved their gait pattern when wearing an orthosis. Orthotics prescriptions were variable even in children with similar gait patterns. This study suggests that quantitative data-driven approaches may provide more clarity and specificity to support orthotics prescription.

scholarly journals Individuals Poststroke Do Not Perceive Their Spatiotemporal Gait Asymmetries as Abnormal

2015 ◽  
Vol 95 (9) ◽  
pp. 1244-1253 ◽  
Author(s):  
Clinton J. Wutzke ◽  
Richard A. Faldowski ◽  
Michael D. Lewek
Keyword(s):  
Step Length ◽  
Gait Pattern ◽  
Conscious Awareness ◽  
Gait Patterns ◽  
Temporal Asymmetry ◽  
Gait Asymmetry ◽  
Stance Time ◽  
The Asymmetry ◽  
Asymmetric Gait

Background Following stroke, spatiotemporal gait asymmetries persist into the chronic phases, despite the neuromuscular capacity to produce symmetric walking patterns. This persistence of gait asymmetry may be due to deficits in perception, as the newly established asymmetric gait pattern is perceived as normal. Objective The purpose of this study was to determine the effect of usual overground gait asymmetry on the ability to consciously and unconsciously perceive the presence of gait asymmetry in people poststroke. Design An observational study was conducted. Methods Thirty people poststroke walked overground and on a split-belt treadmill with the belts moving at different speeds (0%–70% difference) to impose varied step length and stance time asymmetries. Conscious awareness and subconscious detection of imposed gait patterns were determined for each participant, and the asymmetry magnitudes at those points were compared with overground gait. Results For both spatial and temporal asymmetry variables, the asymmetry magnitude at the threshold of awareness was significantly greater than the asymmetry present at the threshold of detection or during overground gait. Participants appeared to identify belt speed differences using the type of gait asymmetry they typically exhibited (ie, step length or stance time asymmetries during overground gait). Limitations Very few individuals with severe spatiotemporal asymmetry were tested, and participants were instructed to identify asymmetric belt speeds rather than interlimb movements. Conclusions The data suggest that asymmetry magnitudes need to exceed usual overground levels to reach conscious awareness. Therefore, it is proposed that the spatiotemporal asymmetry that is specific to each participant may need to be augmented beyond what he or she usually has during walking in order to promote awareness of asymmetric gait patterns for long-term correction and learning.

scholarly journals Gait Patterns in Hemiplegic Patients with Equinus Foot Deformity

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
M. Manca ◽  
G. Ferraresi ◽  
M. Cosma ◽  
L. Cavazzuti ◽  
M. Morelli ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
External Rotation ◽  
Hierarchical Cluster ◽  
Gait Pattern ◽  
Foot Deformity ◽  
Gait Patterns ◽  
Pattern Clustering ◽  
Joint Kinematic ◽  
Rotation Pattern ◽  
Equinus Deformity

Equinus deformity of the foot is a common feature of hemiplegia, which impairs the gait pattern of patients. The aim of the present study was to explore the role of ankle-foot deformity in gait impairment. A hierarchical cluster analysis was used to classify the gait patterns of 49 chronic hemiplegic patients with equinus deformity of the foot, based on temporal-distance parameters and joint kinematic measures obtained by an innovative protocol for motion assessment in the sagittal, frontal, and transverse planes, synthesized by parametrical analysis. Cluster analysis identified five subgroups of patients with homogenous levels of dysfunction during gait. Specific joint kinematic abnormalities were found, according to the speed of progression in each cluster. Patients with faster walking were those with less ankle-foot complex impairment or with reduced range of motion of ankle-foot complex, that is with a stiff ankle-foot complex. Slow walking was typical of patients with ankle-foot complex instability (i.e., larger motion in all the planes), severe equinus and hip internal rotation pattern, and patients with hip external rotation pattern. Clustering of gait patterns in these patients is helpful for a better understanding of dysfunction during gait and delivering more targeted treatment.

scholarly journals Descending and Ascending Signals That Maintain Rhythmic Walking Pattern in Crickets

2021 ◽  
Vol 8 ◽  
Author(s):  
Keisuke Naniwa ◽  
Hitoshi Aonuma
Keyword(s):  
Nervous System ◽  
Gait Pattern ◽  
Gait Patterns ◽  
Control Center ◽  
Walking Gait ◽  
Thoracic Ganglia ◽  
Metathoracic Ganglion ◽  
Tripod Gait ◽  
The Brain ◽  
Leg Movements

The cricket is one of the model animals used to investigate the neuronal mechanisms underlying adaptive locomotion. An intact cricket walks mostly with a tripod gait, similar to other insects. The motor control center of the leg movements is located in the thoracic ganglia. In this study, we investigated the walking gait patterns of the crickets whose ventral nerve cords were surgically cut to gain an understanding of how the descending signals from the head ganglia and ascending signals from the abdominal nervous system into the thoracic ganglia mediate the initiation and coordination of the walking gait pattern. Crickets whose paired connectives between the brain and subesophageal ganglion (SEG) (circumesophageal connectives) were cut exhibited a tripod gait pattern. However, when one side of the circumesophageal connectives was cut, the crickets continued to turn in the opposite direction to the connective cut. Crickets whose paired connectives between the SEG and prothoracic ganglion were cut did not walk, whereas the crickets exhibited an ordinal tripod gait pattern when one side of the connectives was intact. Crickets whose paired connectives between the metathoracic ganglion and abdominal ganglia were cut initiated walking, although the gait was not a coordinated tripod pattern, whereas the crickets exhibited a tripod gait when one side of the connectives was intact. These results suggest that the brain plays an inhibitory role in initiating leg movements and that both the descending signals from the head ganglia and the ascending signals from the abdominal nervous system are important in initiating and coordinating insect walking gait patterns.

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.

Parameterized gait pattern generator based on linear inverted pendulum model with natural ZMP references

2016 ◽  
Vol 32 ◽  
Author(s):  
Ya-Fang Ho ◽  
Tzuu-Hseng S. Li ◽  
Ping-Huan Kuo ◽  
Yan-Ting Ye
Keyword(s):  
Inverted Pendulum ◽  
Center Of Mass ◽  
Gait Pattern ◽  
Gait Patterns ◽  
Gait Planning ◽  
Inverted Pendulum Model ◽  
Pendulum Model ◽  
Lateral Plane ◽  
Planning Algorithm ◽  
Zero Moment

AbstractThis paper presents a parameterized gait generator based on linear inverted pendulum model (LIPM) theory, which allows users to generate a natural gait pattern with desired step sizes. Five types of zero moment point (ZMP) components are proposed for formulating a natural ZMP reference, where ZMP moves continuously during single support phases instead of staying at a fixed point in the sagittal and lateral plane. The corresponding center of mass (CoM) trajectories for these components are derived by LIPM theory. To generate a parameterized gait pattern with user-defined parameters, a gait planning algorithm is proposed, which determines related coefficients and boundary conditions of the CoM trajectory for each step. The proposed parameterized gait generator also provides a concept for users to generate gait patterns with self-defined ZMP references by using different components. Finally, the feasibility of the proposed method is validated by the experimental results with a teen-sized humanoid robot, David, which won first place in the sprint event at the 20th Federation of International Robot-soccer Association (FIRA) RoboWorld Cup.

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.

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