Gait‐based human age classification using a silhouette model

Mansouri, Nabila; Mohammed, Aouled Issa; Yousra, Ben Jemaa

doi:10.1049/iet-bmt.2016.0176

Cited by 25 publications

(17 citation statements)

References 40 publications

(62 reference statements)

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“…A Silhouette Model (SM) descriptor had been proposed for age group classification based on the longitudinal and transverse projections of subjects' silhouette images during the gait cycle. The longitudinal projections describe the stride length, arm swing and body size, while the transverse projection describes the height and posture of subjects [86].…”

Section: ) Gait Age From Model-free Featuresmentioning

confidence: 99%

Learning Age From Gait: A Survey

et al. 2021

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Age is an important human attribute that needs to be determined for various purposes, including security, health, human identification, and law enforcement. Hence, there is an increasing research interest in automatic age estimation using biometric traits such as face and gait. In recent years, gait analysis has received growing attention due to the pervasive nature of video surveillance. Gait signals that measure the manner of walking can be obtained using vision and sensor-based techniques. Individual gait patterns obtainable from videos, images, or sensors are shown unconsciously and are not easily obscured. Additionally, gait signals can be obtained unobtrusively with cameras placed at a long distance because gait does not require high-resolution images. However, the extraction of age-associated gait features is a challenging task due to various gait covariates. These covariates include clothing and view changes for visionbased gait; walking slope and footwear for sensor-based gait. This paper provides a survey of scientific literature on age estimation gait features. We focus on the approaches to extracting age-associated gait features, namely, vision-based and sensor-based approaches, how they may be affected by the different covariates, and domain-specific applications. To make this work useful for as wide of an audience as possible, we also include discussions on key topics such as existing datasets, evaluation strategies, and open challenges that should be addressed in the future.

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Section: ) Gait Age From Model-free Featuresmentioning

confidence: 99%

Learning Age From Gait: A Survey

et al. 2021

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“…İnsanların yürüyüş tarzlarından cinsiyetlerinin tanınması ile ilgili de birçok farklı çalışma vardır (Barclay et al 1978, Barra et al 2019, El-Alfy and Binsaadoon 2019, Xuelong Li et al 2008. Benzer şekilde insanların yürüyüşlerinden kaç yaşında olduğunu tahmin eden farklı çalışmalar da vardır (Xiang , Makihara et al 2011, Mansouri et al 2017, Nabila et al 2017.…”

Section: Literatür Taramasıunclassified

Akıllı Telefonların İvmeölçer Sensörü Yardımıyla Yürüyüş Deseni Analizi

Aydemir¹,

Karslioğlu²

2021

Afyon Kocatepe University Journal of Sciences and Engineering

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Spor alanlarında insan hareketlerini ölçme yeteneği performans ölçüm ve gelişimi için önemli konular arasındadır. Bu durum aynı zamanda klinik değerlendirmelerin de önemli bir parçasıdır. Özellikle elektromanyetik sistemler insan hareketlerini değerlendirmek için en yaygın kullanılan yöntemler arasında yer alır. Buradaki çalışmada 100 metre uzunluğunda bir koridorda 50 farklı kişinin yürüme verileri kullanılmıştır. Yürüme verileri akıllı telefon için geliştirilen bir yazılım ile ivmeölçer sensöründen elde edilmiştir. Verilere üç boyutlu Local Binary Pattern (LBP) yöntemi uygulanmış ve toplam 768 öznitelik çıkarılmıştır. Farklı sınıflandırma algoritmaları ile testler yapılmış ve Subspace KNN ile %97,2 başarılı sınıflandırma elde edilmiştir. Cinsiyete göre yapılan sınıflandırmada ise %99,7 başarılı sınıflandırma elde edilmiştir. Bu yöntem ile yürüme bozukluğu tespitinde yüksek maliyetli cihazlar yerine daha ekonomik yöntemler geliştirileceği düşünülmektedir.

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“…For instance, Lu et al [8] used background subtraction to extract person silhouettes and later employed sparse reconstruction based metric learning to enhance the discriminative feature extraction. Similarly, Nabila et al [29] analyzed both the spatio-temporal traverse and longitudinal silhouette projections within a gait cycle for human age estimation. To enhance the gait characteristics, Lu and Tan [7] utilized a complete set of extracted Gabor features to estimate person age.…”

Section: Related Workmentioning

confidence: 99%

Move Your Body: Age Estimation Based on Chest Movement During Normal Walk

Riaz

Hashmi

et al. 2019

IEEE Access

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The estimation of soft biometrics of a subject, including age, through the gait analysis is a challenging area of research due to variations in individuals' gaits and the effect of ageing on gait patterns. In this paper, we present the results of age estimation based on the analysis of inertial data of human walk. We have recorded 6D accelerations and angular velocities of 86 subjects while performing standardized gait tasks using chest-mounted inertial measurement units. The recorded data were segmented to decompose the long sequences of signals into single steps. For each step, we compute a total of 50 spatio-spectral features from 6D components. We trained three different machine learning classifiers-random forests, support vector machines, and multi-layer perceptron-to estimate the human age. Two different types of cross validation strategies, i.e., tenfold and subjectwise cross validation were employed to gauge the performance of the estimators. The results reveal that it is possible to predict the age of a subject with higher accuracy. With a random forest regressor, when trained and validated on hybrid data, we achieved an average root mean square error of 3.32 years and a mean absolute error of 1.75 years under tenfold cross validation and average root mean square error of 8.22 years under subjectwise cross validation. Since our participants belong to two different demographical regions, i.e., Europe and South Asia, we confirm on broader empirical basis previous findings that age information is present in the human gait. Our proposed approach allows rather robust estimations of age based on the inertial data of a single step, as the used data consist of those collected on different ground surfaces, and the participants were also told to walk pretending different emotional states. The findings on the existing data point out the change of gait while aging, which will also imply that person identification using the gait depends on data that is not too old. INDEX TERMS Age estimation, inertial sensor based age estimation, human gait analysis, smartphone and wearable, soft biometrics.

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Gait‐based human age classification using a silhouette model

Cited by 25 publications

References 40 publications

Learning Age From Gait: A Survey

Learning Age From Gait: A Survey

Akıllı Telefonların İvmeölçer Sensörü Yardımıyla Yürüyüş Deseni Analizi

Move Your Body: Age Estimation Based on Chest Movement During Normal Walk

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