A New View-Calibrated Approach for Abnormal Gait Detection

Lin, Kuo Wei; Wang, Shu Ting; Chung, Pau‐Choo; Yang, Ching Fang

doi:10.1007/978-3-642-35473-1_52

Cited by 1 publication

(1 citation statement)

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“…Model-based systems typically rely on the use of images obtained from multiple calibrated cameras, depth sensing cameras, or a combination of both, to model the gait of an individual [ 12 ]. The use of multiple calibrated cameras allows the systems to estimate features such as the height, distance between the feet [ 13 ], or the joint angles [ 14 ]. Their performance can be further improved by using fish-eye cameras and passive markers to highlight the key joint positions [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification of Gait Impairments Using a Markerless 2D Video-Based System

Verlekar

Soares

Correia

2018

Sensors

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Systemic disorders affecting an individual can cause gait impairments. Successful acquisition and evaluation of features representing such impairments make it possible to estimate the severity of those disorders, which is important information for monitoring patients’ health evolution. However, current state-of-the-art systems perform the acquisition and evaluation of these features in specially equipped laboratories, typically limiting the periodicity of evaluations. With the objective of making health monitoring easier and more accessible, this paper presents a system that performs automatic detection and classification of gait impairments, based on the acquisition and evaluation of biomechanical gait features using a single 2D video camera. The system relies on two different types of features to perform classification: (i) feet-related features, such as step length, step length symmetry, fraction of foot flat during stance phase, normalized step count, speed; and (ii) body-related features, such as the amount of movement while walking, center of gravity shifts and torso orientation. The proposed system uses a support vector machine to decide whether the observed gait is normal or if it belongs to one of three different impaired gait groups. Results show that the proposed system outperforms existing markerless 2D video-based systems, with a classification accuracy of 98.8%.

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