Exportar Publicação

A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA)
Albuquerque, P., Verlekar, T., Correia, P. L.  & Soares, L. D. (2021). A spatiotemporal deep learning approach for automatic pathological Gait classification. Sensors. 21 (18)
Exportar Referência (IEEE)
P. Albuquerque et al.,  "A spatiotemporal deep learning approach for automatic pathological Gait classification", in Sensors, vol. 21, no. 18, 2021
Exportar BibTeX
@article{albuquerque2021_1711644997523,
	author = "Albuquerque, P. and Verlekar, T. and Correia, P. L.  and Soares, L. D.",
	title = "A spatiotemporal deep learning approach for automatic pathological Gait classification",
	journal = "Sensors",
	year = "2021",
	volume = "21",
	number = "18",
	doi = "10.3390/s21186202",
	url = "https://www.mdpi.com/journal/sensors"
}
Exportar RIS
TY  - JOUR
TI  - A spatiotemporal deep learning approach for automatic pathological Gait classification
T2  - Sensors
VL  - 21
IS  - 18
AU  - Albuquerque, P.
AU  - Verlekar, T.
AU  - Correia, P. L. 
AU  - Soares, L. D.
PY  - 2021
SN  - 1424-8220
DO  - 10.3390/s21186202
UR  - https://www.mdpi.com/journal/sensors
AB  - Human motion analysis provides useful information for the diagnosis and recovery assessment of people suffering from pathologies, such as those affecting the way of walking, i.e., gait. With recent developments in deep learning, state-of-the-art performance can now be achieved using a single 2D-RGB-camera-based gait analysis system, offering an objective assessment of gait-related pathologies. Such systems provide a valuable complement/alternative to the current standard practice of subjective assessment. Most 2D-RGB-camera-based gait analysis approaches rely on compact gait representations, such as the gait energy image, which summarize the characteristics of a walking sequence into one single image. However, such compact representations do not
fully capture the temporal information and dependencies between successive gait movements. This limitation is addressed by proposing a spatiotemporal deep learning approach that uses a selection of key frames to represent a gait cycle. Convolutional and recurrent deep neural networks were combined, processing each gait cycle as a collection of silhouette key frames, allowing the system to learn temporal patterns among the spatial features extracted at individual time instants. Trained with gait sequences from the GAIT-IT dataset, the proposed system is able to improve gait pathology classification accuracy, outperforming state-of-the-art solutions and achieving improved generalization on cross-dataset tests.
ER  -