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Verlekar, T. T., Soares, L. D. & Correia, P. L. (2018). Automatic classification of gait impairments using a markerless 2D video-based system. Sensors. 18 (9)

T. T. Verlekar et al.,  "Automatic classification of gait impairments using a markerless 2D video-based system", in Sensors, vol. 18, no. 9, 2018

@article{verlekar2018_1714960525947,
	author = "Verlekar, T. T. and Soares, L. D. and Correia, P. L.",
	title = "Automatic classification of gait impairments using a markerless 2D video-based system",
	journal = "Sensors",
	year = "2018",
	volume = "18",
	number = "9",
	doi = "10.3390/s18092743",
	url = "http://www.mdpi.com/1424-8220/18/9/2743"
}

TY  - JOUR
TI  - Automatic classification of gait impairments using a markerless 2D video-based system
T2  - Sensors
VL  - 18
IS  - 9
AU  - Verlekar, T. T.
AU  - Soares, L. D.
AU  - Correia, P. L.
PY  - 2018
SN  - 1424-8220
DO  - 10.3390/s18092743
UR  - http://www.mdpi.com/1424-8220/18/9/2743
AB  - 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%.
ER  -