Non-Invasive Multi-Camera Gait Analysis System and its Application to Gender Classification

Several studies have examined the accuracy of the Kinect V2 sensor during gait analysis. Usually the data retrieved by the Kinect V2 sensor are compared with the ground truth of certified systems using a Euclidean comparison. Due to the Kinect V2 sensor latency, the application of a uniform temporal alignment is not adequate to compare the signals. On that basis, the purpose of this study was to explore the abilities of the dynamic time warping (DTW) algorithm to compensate for sensor latency (3 samples or 90 ms) and develop a proper accuracy estimation. During the experimental stage, six iterations were performed using the a dual Kinect V2 system. The walking tests were developed at a self-selected speed. The sensor accuracy for Euclidean matching was consistent with that reported in previous studies. After latency compensation, the sensor accuracy demonstrated considerably lower error rates for all joints. This demonstrated that the accuracy was underestimated due to the use of inappropriate comparison techniques. On the contrary, DTW is a potential method that compensates for the sensor latency, and works sufficiently in comparison with certified systems.

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“…An overview of the workspace is shown in Figure 1 . More details about this configuration can be found in our most recent study [ 25 ].…”

Section: Methodsmentioning

confidence: 99%

“…The final walking path (recording area) had a length of 4.80 meters. More details about this configuration can be found in [ 25 ].…”

Section: Figurementioning

confidence: 99%

The Accuracy of the Microsoft Kinect V2 Sensor for Human Gait Analysis. A Different Approach for Comparison with the Ground Truth

Guffanti

Brunete

Hernando

et al. 2020

Sensors

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“…Many researchers have recognized the gender of walking participants [ 23 , 24 , 25 , 26 ], but they have analyzed the results using a comparatively limited dataset, which may adversely affect the system’s accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Gait-Based Real-Time Gender Classification System Using Whole Body Joints

Azhar

Ullah

et al. 2022

Sensors

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Gait-based gender classification is a challenging task since people may walk in different directions with varying speed, gait style, and occluded joints. The majority of research studies in the literature focused on gender-specific joints, while there is less attention on the comparison of all of a body’s joints. To consider all of the joints, it is essential to determine a person’s gender based on their gait using a Kinect sensor. This paper proposes a logistic-regression-based machine learning model using whole body joints for gender classification. The proposed method consists of different phases including gait feature extraction based on three dimensional (3D) positions, feature selection, and classification of human gender. The Kinect sensor is used to extract 3D features of different joints. Different statistical tools such as Cronbach’s alpha, correlation, t-test, and ANOVA techniques are exploited to select significant joints. The Coronbach’s alpha technique yields an average result of 99.74%, which indicates the reliability of joints. Similarly, the correlation results indicate that there is significant difference between male and female joints during gait. t-test and ANOVA approaches demonstrate that all twenty joints are statistically significant for gender classification, because the p-value for each joint is zero and less than 1%. Finally, classification is performed based on the selected features using binary logistic regression model. A total of hundred (100) volunteers participated in the experiments in real scenario. The suggested method successfully classifies gender based on 3D features recorded in real-time using machine learning classifier with an accuracy of 98.0% using all body joints. The proposed method outperformed the existing systems which mostly rely on digital images.

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“…However, one of the major limitations of RGBD cameras is their short range of view (approximately 3 m), which is not enough to analyze human gait [ 11 ]. To solve this problem, several studies have proposed the use of multiple sensors [ 12 , 13 ] or the use of treadmills [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…To solve this problem, several studies have proposed the use of multiple sensors [ 12 , 13 ] or the use of treadmills [ 14 , 15 ]. Multiple sensors have proven to be a valid alternative; however, they require a dedicated laboratory and complicated calibration processes [ 11 ]. On the other hand, the use of treadmills has been shown to disturb the normal gait pattern of patients, as is stated in the study presented by Shi et al [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

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

Guffanti

Brunete

Hernando

et al. 2021

Sensors

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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.

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Non-Invasive Multi-Camera Gait Analysis System and its Application to Gender Classification

Cited by 23 publications

References 38 publications

The Accuracy of the Microsoft Kinect V2 Sensor for Human Gait Analysis. A Different Approach for Comparison with the Ground Truth

The Accuracy of the Microsoft Kinect V2 Sensor for Human Gait Analysis. A Different Approach for Comparison with the Ground Truth

A Gait-Based Real-Time Gender Classification System Using Whole Body Joints

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

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