scholarly journals ROBOGait: A Mobile Robotic Platform for Human Gait Analysis in Clinical Environments

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6786
Author(s):  
Diego Guffanti ◽  
Alberto Brunete ◽  
Miguel Hernando ◽  
Javier Rueda ◽  
Enrique Navarro
Keyword(s):  
Gait Analysis ◽  
Human Gait ◽  
Clinical Settings ◽  
Robotic Platform ◽  
Depth Cameras ◽  
Person Following ◽  
Clinical Environments ◽  
Human Gait Analysis ◽  
Lane Keeping ◽  
Gait Monitoring

Mobile robotic platforms have made inroads in the rehabilitation area as gait assistance devices. They have rarely been used for human gait monitoring and analysis. The integration of mobile robots in this field offers the potential to develop multiple medical applications and achieve new discoveries. This study proposes the use of a mobile robotic platform based on depth cameras to perform the analysis of human gait in practical scenarios. The aim is to prove the validity of this robot and its applicability in clinical settings. The mechanical and software design of the system is presented, as well as the design of the controllers of the lane-keeping, person-following, and servoing systems. The accuracy of the system for the evaluation of joint kinematics and the main gait descriptors was validated by comparison with a Vicon-certified system. Some tests were performed in practical scenarios, where the effectiveness of the lane-keeping algorithm was evaluated. Clinical tests with patients with multiple sclerosis gave an initial impression of the applicability of the instrument in patients with abnormal walking patterns. The results demonstrate that the system can perform gait analysis with high accuracy. In the curved sections of the paths, the knee joint is affected by occlusion and the deviation of the person in the camera reference system. This issue was greatly improved by adjusting the servoing system and the following distance. The control strategy of this robot was specifically designed for the analysis of human gait from the frontal part of the participant, which allows one to capture the gait properly and represents one of the major contributions of this study in clinical practice.

Robotic Platform for Human Gait Analysis

2007 ◽  
Vol 54 (9) ◽  
pp. 1696-1702 ◽  
Author(s):  
Johan van Doornik ◽  
Thomas Sinkjaer
Keyword(s):  
Gait Analysis ◽  
Human Gait ◽  
Robotic Platform ◽  
Human Gait Analysis

DEVELOPMENT OF A SYSTEM FOR HUMAN GAIT ANALYSIS USING KINECT V2

2019 ◽  
Author(s):  
Ítalo Rodrigues ◽  
Jadiane Dionisio ◽  
Rogério Sales Gonçalves
Keyword(s):  
Gait Analysis ◽  
Human Gait ◽  
Kinect V2 ◽  
Human Gait Analysis

scholarly journals Human Gait Analysis in Neurodegenerative Diseases: a Review

2021 ◽  
pp. 1-1
Author(s):  
Grazia Cicirelli ◽  
Donato Impedovo ◽  
Vincenzo Dentamaro ◽  
Roberto Marani ◽  
Giuseppe Pirlo ◽  
...  
Keyword(s):  
Neurodegenerative Diseases ◽  
Gait Analysis ◽  
Human Gait ◽  
Human Gait Analysis

Human Gait Analysis Method Based on Sample Entropy Fusion AlphaPose Algorithm

2021 ◽  
Author(s):  
Xinyu Lv ◽  
Shengying Wang ◽  
Tao Chen ◽  
Jing Zhao ◽  
Desheng Chen ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Sample Entropy ◽  
Human Gait ◽  
Analysis Method ◽  
Human Gait Analysis

scholarly journals Human Gait-labeling Uncertainty and a Hybrid Model for Gait Segmentation

2021 ◽  
Author(s):  
Jiaen Wu ◽  
Henrik Maurenbrecher ◽  
Alessandro Schaer ◽  
Barna Becsek ◽  
Chris Awai Easthope ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Motion Capture ◽  
Ground Truth ◽  
Absolute Error ◽  
Human Gait ◽  
Heel Strike ◽  
Analysis Model ◽  
Flat Foot ◽  
Dynamic Time ◽  
Human Gait Analysis

<div><div><div><p>Motion capture systems are widely accepted as ground-truth for gait analysis and are used for the validation of other gait analysis systems.To date, their reliability and limitations in manual labeling of gait events have not been studied.</p><p><b>Objectives</b>: Evaluate human manual labeling uncertainty and introduce a new hybrid gait analysis model for long-term monitoring.</p><p><b>Methods</b>: Evaluate and estimate inter-labeler inconsistencies by computing the limits-of-agreement; develop a model based on dynamic time warping and convolutional neural network to identify a valid stride and eliminate non-stride data in walking inertial data collected by a wearable device; Gait events are detected within a valid stride region afterwards; This method makes the subsequent data computation more efficient and robust.</p><p><b>Results</b>: The limits of inter-labeler agreement for key</p><p>gait events of heel off, toe off, heel strike, and flat foot are 72 ms, 16 ms, 22 ms, and 80 ms, respectively; The hybrid model's classification accuracy for a stride and a non-stride are 95.16% and 84.48%, respectively; The mean absolute error for detected heel off, toe off, heel strike, and flat foot are 24 ms, 5 ms, 9 ms, and 13 ms, respectively.</p><p><b>Conclusions</b>: The results show the inherent label uncertainty and the limits of human gait labeling of motion capture data; The proposed hybrid-model's performance is comparable to that of human labelers and it is a valid model to reliably detect strides in human gait data.</p><p><b>Significance</b>: This work establishes the foundation for fully automated human gait analysis systems with performances comparable to human-labelers.</p></div></div></div>

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