scholarly journals An Embedded Gait Analysis System for CNS Injury Patients

2019 ◽  
Author(s):  
Gilbert Pradel ◽  
Tong Li ◽  
Didier Pradon ◽  
Nicolas Roche
Keyword(s):  
Gait Analysis ◽  
Cns Injury ◽  
Analysis System

Ultrasonic gait analysis system

1999 ◽  
Vol 21 (2) ◽  
pp. 120
Author(s):  
Yanming Yang ◽  
Fang Lin ◽  
Bo Yuan ◽  
Zheng Li
Keyword(s):  
Gait Analysis ◽  
Analysis System

Validation of wearable inertial sensor-based gait analysis system for measurement of spatiotemporal parameters and lower extremity joint kinematics in sagittal plane

2022 ◽  
pp. 095441192110729
Author(s):  
Gunjan Patel ◽  
Rajani Mullerpatan ◽  
Bela Agarwal ◽  
Triveni Shetty ◽  
Rajdeep Ojha ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Motion Analysis ◽  
Motion Capture ◽  
Inertial Sensor ◽  
Joint Kinematics ◽  
Percentage Error ◽  
Motion Capture System ◽  
Spatiotemporal Parameters ◽  
Analysis System ◽  
Motion Capture Systems

Wearable inertial sensor-based motion analysis systems are promising alternatives to standard camera-based motion capture systems for the measurement of gait parameters and joint kinematics. These wearable sensors, unlike camera-based gold standard systems, find usefulness in outdoor natural environment along with confined indoor laboratory-based environment due to miniature size and wireless data transmission. This study reports validation of our developed (i-Sens) wearable motion analysis system against standard motion capture system. Gait analysis was performed at self-selected speed on non-disabled volunteers in indoor ( n = 15) and outdoor ( n = 8) environments. Two i-Sens units were placed at the level of knee and hip along with passive markers (for indoor study only) for simultaneous 3D motion capture using a motion capture system. Mean absolute percentage error (MAPE) was computed for spatiotemporal parameters from the i-Sens system versus the motion capture system as a true reference. Mean and standard deviation of kinematic data for a gait cycle were plotted for both systems against normative data. Joint kinematics data were analyzed to compute the root mean squared error (RMSE) and Pearson’s correlation coefficient. Kinematic plots indicate a high degree of accuracy of the i-Sens system with the reference system. Excellent positive correlation was observed between the two systems in terms of hip and knee joint angles (Indoor: hip 3.98° ± 1.03°, knee 6.48° ± 1.91°, Outdoor: hip 3.94° ± 0.78°, knee 5.82° ± 0.99°) with low RMSE. Reliability characteristics (defined using standard statistical thresholds of MAPE) of stride length, cadence, walking speed in both outdoor and indoor environment were well within the “Good” category. The i-Sens system has emerged as a potentially cost-effective, valid, accurate, and reliable alternative to expensive, standard motion capture systems for gait analysis. Further clinical trials using the i-Sens system are warranted on participants across different age groups.

Do We Walk Differently at Home? A Context-Aware Gait Analysis System in Continuous Real-World Environments

2021 ◽  
Author(s):  
Nils Roth ◽  
Georg P. Wieland ◽  
Arne Kuderle ◽  
Martin Ullrich ◽  
Till Gladow ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Real World ◽  
Context Aware ◽  
Analysis System ◽  
At Home

Erratum: Biomechanical Parameters in Arch Building Gait Measured by Gait Analysis System with Pressure Sensor

2016 ◽  
Vol 34 (2) ◽  
pp. 195
Author(s):  
Bae Youl Lee ◽  
Seung Don Yoo ◽  
Seung Ah Lee ◽  
JinMann Chon ◽  
Dong Hwan Kim ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Pressure Sensor ◽  
Biomechanical Parameters ◽  
Analysis System

scholarly journals Development of the 4D Gait Analysis System with the Locus of Center of Pressure on Sole of Foot

2010 ◽  
Vol 12 (4) ◽  
pp. 527-531
Author(s):  
Hideo Kawakami ◽  
Nobuhiko Sugano ◽  
Hidenobu Miki ◽  
Kazuo Yonenobu ◽  
Asaki Hattori ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Center Of Pressure ◽  
Analysis System

scholarly journals Remote Gait Type Classification System Using Markerless 2D Video

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1824
Author(s):  
Pedro Albuquerque ◽  
João Pedro Machado ◽  
Tanmay Tulsidas Verlekar ◽  
Paulo Lobato Correia ◽  
Luís Ducla Soares
Keyword(s):  
Gait Analysis ◽  
Learning Strategies ◽  
Web Application ◽  
State Of The Art ◽  
The State ◽  
Remote Healthcare ◽  
Visual Artifacts ◽  
Analysis System ◽  
Private Network ◽  
User Friendly

Several pathologies can alter the way people walk, i.e., their gait. Gait analysis can be used to detect such alterations and, therefore, help diagnose certain pathologies or assess people’s health and recovery. Simple vision-based systems have a considerable potential in this area, as they allow the capture of gait in unconstrained environments, such as at home or in a clinic, while the required computations can be done remotely. State-of-the-art vision-based systems for gait analysis use deep learning strategies, thus requiring a large amount of data for training. However, to the best of our knowledge, the largest publicly available pathological gait dataset contains only 10 subjects, simulating five types of gait. This paper presents a new dataset, GAIT-IT, captured from 21 subjects simulating five types of gait, at two severity levels. The dataset is recorded in a professional studio, making the sequences free of background camouflage, variations in illumination and other visual artifacts. The dataset is used to train a novel automatic gait analysis system. Compared to the state-of-the-art, the proposed system achieves a drastic reduction in the number of trainable parameters, memory requirements and execution times, while the classification accuracy is on par with the state-of-the-art. Recognizing the importance of remote healthcare, the proposed automatic gait analysis system is integrated with a prototype web application. This prototype is presently hosted in a private network, and after further tests and development it will allow people to upload a video of them walking and execute a web service that classifies their gait. The web application has a user-friendly interface usable by healthcare professionals or by laypersons. The application also makes an association between the identified type of gait and potential gait pathologies that exhibit the identified characteristics.

A practical gait analysis system using gyroscopes

1999 ◽  
Vol 21 (2) ◽  
pp. 87-94 ◽  
Author(s):  
Kaiyu Tong ◽  
Malcolm H Granat
Keyword(s):  
Gait Analysis ◽  
Analysis System

Spinal kinematics in horses with induced back pain

2009 ◽  
Vol 22 (06) ◽  
pp. 448-454 ◽  
Author(s):  
C. B. Gómez Álvarez ◽  
R. Meulenbelt ◽  
C. Johnston ◽  
P. R. van Weeren ◽  
K. Roethlisberger-Holm ◽  
...  
Keyword(s):  
Back Pain ◽  
Gait Analysis ◽  
Poor Performance ◽  
Underlying Mechanism ◽  
Unaffected Side ◽  
Longissimus Dorsi Muscle ◽  
Consistent Change ◽  
Analysis System ◽  
Back Problems ◽  
The Relationship

SummaryBack problems are important contributors to poor performance in sport horses. It has been shown that kinematic analysis can differentiate horses with back problems from asymptomatic horses. The underlying mechanism can, however, only be identified in a uniform, experimental setting.Our aim was to determine if induction of back pain in a well-defined site would result in a consistent change in back movement.Back kinematics were recorded at a walk and trot on a treadmill. Unilateral back pain was then induced by injecting lactic acid into the left longissimus dorsi muscle. Additional measurements were done subsequent to the injections. Data were captured during steady state locomotion at 240 Hz using an infraredbased gait analysis system.After the injections, the caudal thoracic back was more extended at both gaits. The back was also bent more to the left at both gaits. However, at the walk, there was a reversed pattern after a week with bending of the back to the unaffected side.Horses with identical back injuries appear to show similar changes in their back kinematics, as compared to the asymptomatic condition. Unilateral back pain seems to result in an increased extension of the back, as well as compensatory lateral movements.Back movements are complex and subtle, and changes are difficult to detect with the human eye. Present-day gait analysis systems can identify changes in the back movement, and knowledge of the relationship between such changes and the site of injury will be of help in better localising and diagnosing disorders of the equine back.

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