Sign language recognition using real-sense

Huang, Jie; Zhou, Wengang; Li, Houqiang; Li, Weiping

doi:10.1109/chinasip.2015.7230384

Cited by 41 publications

(20 citation statements)

References 7 publications

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“…However, gesture recognition is a very challenging task due to the complexity and large variations in hand postures. Huang et al [98] proposes a method for sign language recognition which involves the use of a DNN fed with real-sense data. The DNN takes the 3-D coordinates of finger joints as inputs directly with no handcrafted features used.…”

Section: Pervasive Sensing For Health and Wellbeingmentioning

confidence: 99%

Deep Learning for Health Informatics

Ravì

Wong

Deligianni

et al. 2017

IEEE J. Biomed. Health Inform.

1,563

776

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With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on machine learning in health informatics. Deep learning, a technique with its foundation in artificial neural networks, is emerging in recent years as a powerful tool for machine learning, promising to reshape the future of artificial intelligence. Rapid improvements in computational power, fast data storage, and parallelization have also contributed to the rapid uptake of the technology in addition to its predictive power and ability to generate automatically optimized high-level features and semantic interpretation from the input data. This article presents a comprehensive up-to-date review of research employing deep learning in health informatics, providing a critical analysis of the relative merit, and potential pitfalls of the technique as well as its future outlook. The paper mainly focuses on key applications of deep learning in the fields of translational bioinformatics, medical imaging, pervasive sensing, medical informatics, and public health.

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Section: Pervasive Sensing For Health and Wellbeingmentioning

confidence: 99%

Deep Learning for Health Informatics

Ravì

Wong

Deligianni

et al. 2017

IEEE J. Biomed. Health Inform.

1,563

776

View full text Add to dashboard Cite

show abstract

“…(8) In this formula, ||x − y|| is a norm over the elements of the two sets and obviously the contour pixels (x, y) run over the set of indices i,j = 1,..., N. In our case, this norm is taken to be the Euclidean distance between the two groups of feature vector elements. Since the original definition of the Hausdorff distance is rather sensitive to noise, we opted to use a more robust version of this metric, namely the Modified Hausdorff Distance (MHD), as proposed originally in [16].…”

Section: Hand Gesture Supervised Classificationmentioning

confidence: 99%

“…Sensor-based methods are not natural because the user must wear cumbersome instruments, while camera-based methods require effective algorithms to track and segment hand signatures from complex background. To address these problems, the authors in [8] proposed a novel method for SLR that involves the use of the Real-Sense. Their approach is based on a camera device that detects and tracks the location of hands in a natural way.…”

Section: Introductionmentioning

confidence: 99%

Hand gesture recognition in confined spaces with partial observability and occultation constraints

Shirkhodaie

Chan

2016

SPIE Proceedings

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Human activity detection and recognition capabilities have broad applications for military and homeland security. These tasks are very complicated, however, especially when multiple persons are performing concurrent activities in confined spaces that impose significant obstruction, occultation, and observability uncertainty. In this paper, our primary contribution is to present a dedicated taxonomy and kinematic ontology that are developed for in-vehicle group human activities (IVGA). Secondly, we describe a set of hand-observable patterns that represents certain IVGA examples. Thirdly, we propose two classifiers for hand gesture recognition and compare their performance individually and jointly. Finally, we present a variant of Hidden Markov Model for Bayesian tracking, recognition, and annotation of hand motions, which enables spatiotemporal inference to human group activity perception and understanding. To validate our approach, synthetic (graphical data from virtual environment) and real physical environment video imagery are employed to verify the performance of these hand gesture classifiers, while measuring their efficiency and effectiveness based on the proposed Hidden Markov Model for tracking and interpreting dynamic spatiotemporal IVGA scenarios.

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“…Estas interfaces se componen de un sensor, normalmente un electrodo encargado de adquirir las señales de la actividad cerebral o muscular, una etapa de amplificación y filtrado, y un sistema de control [80]. [88] proponen un sistema para mejorar la comunicación de personas sordomudas basado en DNNs para el reconocimiento e interpretación del lenguaje de señas usando el método real-sense, que toma las coordenadas en 3D de las articulaciones de los dedos y detecta el movimiento de manera más natural que otros métodos, como el Kinect o el uso de guantes con sensores. P. Pouladzadeh et al [89] y P. Kuhad, A. Yassine Y S. Shimohammadi [90] proponen un sistema para la medición de calorías con el objetivo de ayudar a pacientes y médicos en la prevención de enfermedades relaciones con la dieta, como la obesidad.…”

Section: Interfaces Cerebro-máquina Y Hombremáquinaunclassified

Aplicaciones de las redes neuronales y el deep learning a la ingeniería biomédica

Ramos¹

2020

revuin

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Hoy en día, las redes neuronales artificiales y el deep learning, son dos de las herramientas más poderosas del aprendizaje de máquina, que tienen por objetivo desarrollar sistemas que aprenden automáticamente, reconocen patrones, predicen comportamientos y generalizan información a partir de conjuntos de datos. Estasdos herramientas se han convertido en un potencial campo de investigación con aplicaciones a la ingeniería, no siendo la ingeniería biomédica la excepción. En este artículo se presenta una revisión actualizada de las principales aplicaciones de las redes neuronales y el deep learning a la ingeniería biomédica en las ramas de la ómica, la imagenología, las interfaces cerebro-máquina y hombre-máquina, y la gestión y administración de la salud pública; ramas que se extienden desde el estudio de procesos a nivel molecular, hasta procesos que involucran grandes poblaciones.Palabras clave:aprendizaje de máquina; inteligencia artificial; reconocimiento de patrones; ómica; bioinformática; biomedicina; imagenología; interfaces cerebro-máquina; interfaces hombre-máquina;salud pública.

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Sign language recognition using real-sense

Cited by 41 publications

References 7 publications

Deep Learning for Health Informatics

Deep Learning for Health Informatics

Hand gesture recognition in confined spaces with partial observability and occultation constraints

Aplicaciones de las redes neuronales y el deep learning a la ingeniería biomédica

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