Comparison of temporal and kinetic walking parameters among young people and falling and non-falling elderly persons

Briani, Ronaldo Valdir; Taborda, Bianca; Martines, Érica Caroline Carvalho; Aragão, Fernando Amâncio

doi:10.1590/1809-9823.2015.14153

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…Hence, this paper introduces a new type of gait-based human age descriptor relying on medical and biomechanical researches [36][37][38] that confirm that the arms' swing, the head pitch, the hunched posture and the stride's length are among the most outstanding ageing characteristics. Proposed descriptor represents spatiotemporal gait variations and takes advantage of these features for better characterisation of each age class.…”

Section: Introductionmentioning

confidence: 94%

Gait‐based human age classification using a silhouette model

2017

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Age estimation at a distance has potential applications including visual surveillance and monitoring in public places. Far from the camera, image resolution is significantly degraded. In fact, age estimation using classical methods such as face is not reliable. Given that gait is very sensitive to ageing, gait analysis is the suitable solution for age estimation at a great distance from the camera. Medical and biomechanical studies prove that older adults adapt their walking toward a safer and more stable gait and an established balance. Indeed, in this study the authors propose a gait-based descriptor for age classification using a silhouette projection model. The proposed model encapsulates both spatiotemporal longitudinal (SLP) and transverse (STP) projections of the silhouette during a gait cycle. The proposed model aims to represent the arms' swing, the head's pitch, the hunched posture and the stride's length, which are among the most outstanding ageing characteristics that appear on the elderly's gait. Although age classification using gait is a very challenging task, SLP and STP curves analysis shows a considerable discrimination between young and elderly people. Also, experiments conducted on the OU-ISIR database prove that their proposed descriptor outperforms existing ones by reaching an important recognition rate.

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Section: Introductionmentioning

confidence: 94%

Gait‐based human age classification using a silhouette model

2017

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“…Hence, in this paper we propose a new model‐free gait‐based human age descriptor relying on medical and biomechanical researches [45–47] that confirm that arms swing, head pitch, hunched posture and stride length are among the most outstanding ageing characteristics. The proposed descriptor represents spatiotemporal gait variations and take advantage of these features for better characterisation of each age class.…”

Section: Related Workmentioning

confidence: 99%

Gait features fusion for efficient automatic age classification

Mansouri

Issa

Jemâa

2017

IET Computer Vision

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Far from the camera, image resolution is significantly degraded and person cannot cooperate with the acquisition equipment. So, the classical intrusive biometrics approach could not be applied. As a non-intrusive biometric, gait analysis gained the attention of the computer vision community for number of potential applications such as age estimation. Since, that gait is very sensitive to ageing, gait analysis is the suitable solution for age estimation at a great distance from the camera. Given the complexity of this task, the authors propose in this study a new approach based on descriptors cascade. The proposed approach is to use a fusion of some efficient contour and silhouette descriptors. Indeed, they introduce the proposed descriptor based on silhouette projection model (SM) in the first time. In the second time, the proposed descriptor is merged with the best existing ones in order to enhance the classification performances. Despite that age classification using gait is a very challenging task, experiments conducted on OU-ISIR database show that their proposed descriptors fusion approach enhances considerably the recognition rate.

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“…Bio-signals like DNA of a person, ECG signals are highlighted for gender recognition. So, with the help of different features, the perception of gender can be performed in an efficient and abrupt manner [4].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Gender using Gait Energy Image Projections Based on Random Forest Classifier

Hema*¹,

Babulu²,

Balaji³

2019

IJITEE

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Identification of gender is a very fascinating criterion in the present day scenario. Especially, in the surveillance applications, gender recognition is very beneficial. With the use of face, speech, voice and gait, the gender of a person can be determined. Non-contact, non-invasive and easily acquired at distance, gait analysis has attracted the interest of many researchers in the classification of gender. For the identification of gender, 2 stages of the methodology are used in our proposed work. A new descriptor called Gait energy image projection model(GPM) is proposed which highlights all the gender-related parameters. In the second stage of methodology, proposed descriptor GPM is fused with already existing descriptors like GEI and FED for enhanced performance. For classifying the gender, an Ensemble classifier called Random Forests is applied to the individual and fused descriptors and the results are evaluated. Two datasets are used for experimentation namely CASIA B and OU-ISIR datasets which are standard datasets for person identification and different performance metrics such as accuracy, precision, recall and error rate are evaluated.

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Comparison of temporal and kinetic walking parameters among young people and falling and non-falling elderly persons

Cited by 4 publications

References 18 publications

Gait‐based human age classification using a silhouette model

Gait‐based human age classification using a silhouette model

Gait features fusion for efficient automatic age classification

Recognition of Gender using Gait Energy Image Projections Based on Random Forest Classifier

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