Gait dataset

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Gait Recognition

If nothing happens, download the GitHub extension for Visual Studio and try again. Subfolders "aaa", "cxb", "lbr", Subfolders store data for 15 different users, the file name of a single data subfile is named after the rule of "tester's abbreviation" "m 1to9 ""attr" "count". The kinect of 9 gestures corresponding to the above acceleration data can be downloaded here. The file tree of this dataset is shown in "File Tree of "Kinect data of 9 gestures"".

Both types of data are recorded simultaneously The file structure of each video subfolder is as follows:. Skip to content.

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OriginalCalibratedText This folder contains human body data without any calibration.Gait recognition has received increasing attention as a remote biometric identification technology, i. Motion Tracking and Gesture Recognition. Gait recognition has been paid lots of attention as one of the biometric identification technologies. Since these advantages, gait recognition is expected to be applied in scenarios, such as criminal investigation and access control.

The ways of acquiring the original gait data depend on how to recognize the gait. Usually the gait is acquired by single camera, multiple cameras, professional motion capture system e. VICON and camera with depth sensor e. Since human gait is a kind of periodic signal, a gait sequence may include several gait cycles.

Gait period extraction is helpful to reduce the data redundancy because all the gait features can be included in one whole gait cycle. Various gait features are used in different kinds of gait recognition methods and they influence the performance of gait recognition. Gait classification, i. Cunado et al. Johnson et al. Guo et al. Using this model, the human motion can be recorded as a sequence of stick figure parameters, which can be the input of BP neural network.

gait dataset

Tanawongsuwan et al. Wang et al. Specifically, the static body feature is in a form of a compact representation obtained by Procrustes shape analysis.

Gait energy image GEI [ 7 ] is the most popular gait representation, which represents the spatial and temporal gait information in a grey image. The intensity of an MSI represents motion information during one gait cycle.

Because GEI and MSI represent both motion and appearance information, they are sensitive to the changes in various covariate conditions such as carrying and clothing. SVB frieze pattern projects the silhouettes horizontally and vertically to represent the gait information, and uses key frame subtraction to reduce the effects of appearance changes on the silhouettes.

Gait entropy image GEnI [ 10 ] is another gait representation, which is based on Shannon entropy. They utilize a colour mapping function to encode each gait contour image in the same gait sequence, and average over a quarter gait cycle to one CGI.

It is helpful to preserve more temporal information of a gait cycle. There are other methods to keep temporal information of gait sequences, which have good performance too. Sundaresan et al.Once production of your article has started, you can track the status of your article via Track Your Accepted Article. Search in:. Submit Your Paper. Supports Open Access. View Articles. Track Your Paper Check submitted paper Check the status of your submitted manuscript in the submission system Track accepted paper Once production of your article has started, you can track the status of your article via Track Your Accepted Article.

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gait dataset

CiteScore values are based on citation counts in a given year e. Impact Factor: 2. View More on Journal Insights. Your Research Data Share your research data. This free service is available to anyone who has published and whose publication is in Scopus.

Researcher Academy Author Services Try out personalized alert features. Mendeley Data Repository is free-to-use and open access. It enables you to deposit any research data including raw and processed data, video, code, software, algorithms, protocols, and methods associated with your research manuscript.

Your datasets will also be searchable on Mendeley Data Search, which includes nearly 11 million indexed datasets. For more information, visit Mendeley Data. Data for: Forward and backward walking in Parkinson disease: A factor analysis.

This excel file contains raw gait parameter data for Parkinson disease patients who performed forward and backward walking both OFF and ON levodopa medications. This file also contains control data. This dataset summarizes findings from our review of the literature. All analyses in our paper our performed on this dataset. Data for: The influence of different technical marker sets upon hip kinematics during gait.

The data provided within this file details and compares hip joint kinematics calculated using different technical marker sets, the specific technical marker sets and the code used to describe these within the file is provided within the first worksheet. Data for: The effects of running a km race on neuromuscular performance measures in recreationally competitive runners.

Biomechanics data for "The effects of running a km race on neuromuscular performance measures in recreationally competitive runners". Data for: How normal is normal: consequences of stride to stride variability, treadmill walking and age when using normative paediatric gait data. This workbook contains supplementary material for the article: How normal is normal: Consequences of stride to stride variability, treadmill walking and age when using normative pediatric gait data.

Each of the worksheets contains data from the treadmill and overground walking conditions. Consecutive blue shading defines Treadmill data. Data for: Does Dynamic Tape change the walking biomechanics of women with greater trochanteric pain syndrome? A double-blind randomised controlled crossover trial.

Supplementary data S1.Thank you for visiting nature. You are using a browser version with limited support for CSS.

Walking gait dataset

To obtain the best experience, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. A Nature Research Journal. Human motion capture is used in various fields to analyse, understand and reproduce the diversity of movements that are required during daily-life activities. The proposed dataset of human gait has been established on 50 adults healthy and injury-free for lower and upper extremities in the most recent six months, with no lower and upper extremity surgery in the last two years.

Participants were asked to walk on a straight level walkway at 5 speeds during one unique session: 0—0. Three dimensional trajectories of 52 reflective markers spread over the whole body, 3D ground reaction forces and moment, and electromyographic signals were simultaneously recorded.

For each participants, a minimum of 3 trials per condition have been made available in the dataset for a total of trials. This dataset could increase the sample size of similar datasets, lead to analyse the effect of walking speed on gait or conduct unusual analysis of gait thanks to the full body markerset used.

Dataset on gait patterns in degenerative neurological diseases

Machine-accessible metadata file describing the reported data ISA-Tab format. Human motion capture is nowadays commonly used in various fields to analyse, understand and reproduce the diversity of movements that can be produced during daily-life activities. In clinical practice, the emergence of evidence-based medicine promoted the development of quantitative assessment tools for the diagnosis and treatment of pathology-related movement disorders.

In particular, the process of gait disorders analysis currently often consists of the measurement of joint kinematics and kinetics in three dimensions 1.

This assessment is called clinical gait analysis CGA and attempts to provide an objective record that quantifies the magnitude of deviations from normal gait 2. On this basis, a set of pathology-related impairments having the most impact on gait is identified and can be used to target the treatment 3. However, the identification of deviations is highly dependent with the characteristics of the normative database used 4.

Special attention is then required to discriminate the differences between pathological and asymptomatic populations that could confound deviations. In particular, the gait of pathological populations is often observed at their own self-selected walking speed and compared to normative data established at the spontaneous walking speed of an asymptomatic population 5.

Since the spontaneous walking speed of pathological populations e. Because walking speed is known to affect kinematics, kinetics, spatiotemporal parameters and muscular activity 8the identification of gait deviations can then become challenging since both pathology and walking speed difference may contribute to them 9.

But walking speed is not the only variable that could be source of a mismatch in comparison of a patient and an asymptomatic population.

The Latest Mendeley Data Datasets for Gait & Posture

Demographic and anthropometric parameters may also affect CGA interpretation. Recently, Chehab et al.

gait dataset

While walking speed was the most influential variable, the authors highlighted the influence of demographic and anthropometric parameters on very common parameters e.

Several datasets have been made available in the literature and can be used to ease the establishment of a broad normative database allowing to match patient characteristics 111213 However, few datasets include all the common parameters on a large number of subjects i.Gait analysis GA has been widely used to better understand the gait patterns of a wide range of populations.

The application of this method has the ability to distinguish between normal and abnormal gaits Gage et al. These measures are objective and are typically performed using a three-dimensional 3D motion-capture system and force plates. A typical clinical study commonly approaches GA by comparing a group of pathological e. However, the control group usually consists of a small number of age-matched individuals, each walking at a comfortable speed, which is commonly faster than that of individuals in the pathological group Marrocco et al.

Therefore, the validity of these studies is limited by the potential bias caused by the difference in gait speeds. A possible solution to this problem is to perform walking trials at a wider range of gait speeds, from very slow to very fast, to enable comparisons that are less biased. Previous studies have reported speed dependency in kinematics and kinetics data during overground walking Bovi et al.

However, the authors of these studies provided only the average and standard deviation data across participants, and no raw data were publicly available with which to validate the inferences made by the studies. In fact, recently, data sharing and increased acceptance of replication studies have been advocated to overcome the aforementioned limitations and to validate the inferences made by previous gait studies Ferber et al. Furthermore, other studies have advocated the need to share data and the importance of a normative database Winter, to improve the interpretation of GA outcomes.

In the early s, Winter began to make gait datasets available in his book Winter, ; however, the only data provided were those of a single healthy subject. To address these limitations, this study aimed to create a publicly available dataset of 3D walking kinematics and kinetics data on healthy young and older adults at a range of gait speeds in both the treadmill and overground environments.

To generate data for the dataset, we measured the kinematics and kinetics of participants walking at various speeds both overground and on a treadmill. Study participants included 42 volunteers, including 24 young adults age All participants were free of any lower-extremity injury in the last six months before the data were collected, and all were free of any orthopedic or neurologic disease that could interfere with their gait patterns.

In order to train with the equipment and design appropriate procedures, a pilot study was conducted first with five participants. The provided metadata file, WBDSinfo.

Prior to the collection of data, each participant read and signed a consent form that had previously been approved by the university ethics committee CAAE: The instrumented treadmill has handrails alongside it attached directly to split mounting plates.

Therefore, while the subjects may hold the handrails during gait, the measured forces include only the forces applied by the legs during stance. All gait trials were performed in barefoot conditions, and the participants wore comfortable shorts women also wore sports bras.

In addition, the participants walked on the treadmill at eight different controlled speeds, which are described below. Previously, a computerized random-number generator had been used to define the order of the walking trials on the treadmill. The marker-set protocol adopted for this study comprised 26 anatomical reflective markers Leardini et al.To browse Academia.

Skip to main content. Log In Sign Up. Human identification based on the reduced kinematic data of the gait Konrad Wojciechowski. Human identification based on the reduced kinematic data of the gait. In the first freedom. For a typical skeleton model with 22 defined stage the pose descriptions of the given skeleton model are segments and one extra global skeleton rotation, the reduced by the linear principal component analysis.

We description contains up to 69 Euler angles or 23 quaternions. Afterwards, we use two approaches: feature extraction and dynamic time warping.

In the feature It is believed that human movements and especially their extraction the Fourier transform with low pass filtering is single type - gait, can be represented in much lower applied. To suppress the gait dynamic Fourier components dimensional feature spaces. It is so because the rotation for the velocities and accelerations are calculated.

Such angles of the pose are highly correlated and can be reduced processing transforms gait's data into the vector features without the loss of information. The crucial task is the way of space, in which the supervised learning is used to identify reduction, which should give short descriptions and store as humans.

To discover most valuable features - principal and much information as possible. The important advantage of the Fourier components, PCA values, velocities and accelerations reduced features spaces is the fact that they have much less and to improve the classification, we prepare the features redundant and noisy data, which makes usually the recognition selection scenarios and observe the identification efficiency.

To evaluate the proposed method we have collected gait process easier and more efficient. We use preprocessing filters to detect the main double step and to scale time domain Gait can be captured by a stereovision system of two- to the given number of motion frames. We have obtained dimensional video cameras of typical monitoring systems.The dataset has been established to enable comparative studies on gait analysis, especially the problems of gait index estimation and abnormal gait detection.

The dataset includes 9 gaits that are normal symmetric walking gait and 8 simulated abnormal asymmetric ones. Since the dataset contains 3 synchronized data types point cloud, skeleton, frontal silhouettethis is appropriate for assessment on many gait-related methods.

This page briefly describes the dataset and provides the download links. Data description The dataset was acquired by the system containing: Microsoft Kinect 2 with Time-of-Flight depth estimation Two flat mirrors placed behind the subject Treadmill placed at the center for performing walking gaits Acquired walking gaits: There were 9 volunteers in our dataset, in which each subject performed 9 walking gaits with different levels of symmetry.

Each walking gait was acquired in consecutive frames. Any researcher reporting results which use this dataset is requested to cite the following paper : T.

Pyramidal Fisher Motion for Multiview Gait Recognition

Nguyen, H. Huynh and J. Nguyen and H. Download links Notice: the order of gaits in the following files is the same with the table Point cloud:. Skeleton: skeletons. Silhouette: silhouettes. Normalized cylindrical histogram: normhists.