Predicting body movements for person identification under different walking conditions

Nguyen, Duc-Phong; Phan, Cong-Bo; Koo, Seungbum

doi:10.1016/j.forsciint.2018.07.022

Cited by 9 publications

(3 citation statements)

References 28 publications

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“…Marker-based clinical gait motion capture systems can provide three-dimensional rotations of a full-body human model including the rotation along the segment longitudinal axis [28]; however, the cost of obtaining motion capture data is a bottleneck for collecting a large number of samples [29]. Meanwhile, three-dimensional gait data obtained through a 19-point human stick model are commonly applied for computational human motion studies [16,[30][31][32][33][34][35]. When the positions of the human stick model were sampled at 101 time points of a gait cycle, the 3D gait data had dimensions of 19 × 3 × 101 = 5757.…”

Section: Related Studiesmentioning

confidence: 99%

Anomalous Gait Feature Classification From 3-D Motion Capture Data

Jeon

Lee

Koo

2022

IEEE J. Biomed. Health Inform.

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The gait kinematics of an individual is affected by various factors, including age, anthropometry, gender, and disease. Detecting anomalous gait features aids in the diagnosis and treatment of gait-related diseases. The objective of this study was to develop a machine learning method for automatically classifying five anomalous gait features, i.e., toe-out, genu varum, pes planus, hindfoot valgus, and forward head posture features, from threedimensional data on gait kinematics. Gait data and gait feature labels of 488 subjects were acquired. The orientations of the human body segments during a gait cycle were mapped to a low-dimensional latent gait vector using a variational autoencoder. A two-layer neural network was trained to classify five gait features using logistic regression and calculate an anomalous gait feature vector (AGFV). The proposed network showed balanced accuracies of 82.8% for a toe-out, 85.9% for hindfoot valgus, 80.2% for pes planus, 73.2% for genu varum, and 92.9% for forward head posture when the AGFV was rounded to the nearest zero or 1. Multiple anomalous gait features were detectable using the proposed method, which has a practical advantage over current gait indices, including the gait deviation index with a single value. The overall results confirmed the feasibility of using the proposed method for screening subjects with anomalous gait features using three-dimensional motion capture data.

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Section: Related Studiesmentioning

confidence: 99%

Anomalous Gait Feature Classification From 3-D Motion Capture Data

Jeon

Lee

Koo

2022

IEEE J. Biomed. Health Inform.

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“…A person’s gait is referred to as his or her style or pattern of walking. Numerous factors, such as sex [ 1 ], age [ 2 , 3 ], walking speed [ 1 , 4 , 5 ], and type of disease, can affect gait. As gait patterns can be quite characteristic in certain disorders, such as the typical shuffling gait in parkinsonism, analyzing gait patterns can be very helpful in establishing medical diagnoses.…”

Section: Introductionmentioning

confidence: 99%

Automated Gait Analysis Based on a Marker-Free Pose Estimation Model

Hii

Beng

Zainal

et al. 2023

Sensors

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Gait analysis is an essential tool for detecting biomechanical irregularities, designing personalized rehabilitation plans, and enhancing athletic performance. Currently, gait assessment depends on either visual observation, which lacks consistency between raters and requires clinical expertise, or instrumented evaluation, which is costly, invasive, time-consuming, and requires specialized equipment and trained personnel. Markerless gait analysis using 2D pose estimation techniques has emerged as a potential solution, but it still requires significant computational resources and human involvement, making it challenging to use. This research proposes an automated method for temporal gait analysis that employs the MediaPipe Pose, a low-computational-resource pose estimation model. The study validated this approach against the Vicon motion capture system to evaluate its reliability. The findings reveal that this approach demonstrates good (ICC(2,1) > 0.75) to excellent (ICC(2,1) > 0.90) agreement in all temporal gait parameters except for double support time (right leg switched to left leg) and swing time (right), which only exhibit a moderate (ICC(2,1) > 0.50) agreement. Additionally, this approach produces temporal gait parameters with low mean absolute error. It will be useful in monitoring changes in gait and evaluating the effectiveness of interventions such as rehabilitation or training programs in the community.

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“…The past decade has seen an increase in using footprints for biometric authentication. Footprints are universal, easy to retrieve and have not changed much throughout time [2]. Using footprints for biometrics is not as extensive as other biometric traits, yet it still has its own applications.…”

Section: Introductionmentioning

confidence: 99%

Footprint biometric authentication using SqueezeNet

Safie

Ramli

2023

IJEECS

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Biometric authentication is a process of identity verification once an identity is claimed by an individual. It uses unique features on the human body. Footprints are a new biometric feature that has sparked interest among researchers, as this feature is universal, easy to extract and has not changed throughout time. The focus of researchers in this field is to improve the recognition rate. Various techniques have been developed for this purpose, but the accuracy percentage is at 98% with an equal error rate (EER) of 6.1%. This paper proposes the use of a new technique called SqueezeNet in classifying footprint images. SqueezeNet belongs to the convolutional neural network (CNN) family. In this study, 300 footprint images were used from 15 individuals. The 70% of these images were used to train the proposed SqueezeNet network, while the rest were used for testing. At the end of this simulation, SqueezeNet has achieved an accuracy of 98.67% with an EER of 2.1%.

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Predicting body movements for person identification under different walking conditions

Cited by 9 publications

References 28 publications

Anomalous Gait Feature Classification From 3-D Motion Capture Data

Anomalous Gait Feature Classification From 3-D Motion Capture Data

Automated Gait Analysis Based on a Marker-Free Pose Estimation Model

Footprint biometric authentication using SqueezeNet

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