Interval Fuzzy Rule-Based Hand Gesture Recognition

Bedregal, Benjamín R. C.; Dimuro, Graçaliz Pereira; Costa, Á. S.

doi:10.1109/scan.2006.25

Cited by 17 publications

(5 citation statements)

References 31 publications

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“…To reduce inexactitudes, most gloves need to be calibrated for a specific user [ 81 , 90 ]. Calibration is usually accomplished by requesting that the user put his/her hands in specified gestures (e.g., flat hand, flex the hand a few times, fist) [ 13 , 42 ]. Number of sensors: The real challenge of trajectory detection is to measure the common complementary function for SLR using a limited number of sensors [ 77 , 78 ].…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, numerous kinds of applications are currently involved in gesture recognition systems such as SLR, substitutional computer interfaces, socially assistive robotics, immersive gaming, virtual objects, remote control, medicine-health care, gesture recognition of hand/body language, etc. [ 10 , 11 , 12 , 13 , 14 , 15 ]. These benefits are described in detail in Section 5.1 .…”

Section: Introductionmentioning

confidence: 99%

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A Review on Systems-Based Sensory Gloves for Sign Language Recognition State of the Art between 2007 and 2017

Ahmed

Zaidan

et al. 2018

Sensors

188

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Loss of the ability to speak or hear exerts psychological and social impacts on the affected persons due to the lack of proper communication. Multiple and systematic scholarly interventions that vary according to context have been implemented to overcome disability-related difficulties. Sign language recognition (SLR) systems based on sensory gloves are significant innovations that aim to procure data on the shape or movement of the human hand. Innovative technology for this matter is mainly restricted and dispersed. The available trends and gaps should be explored in this research approach to provide valuable insights into technological environments. Thus, a review is conducted to create a coherent taxonomy to describe the latest research divided into four main categories: development, framework, other hand gesture recognition, and reviews and surveys. Then, we conduct analyses of the glove systems for SLR device characteristics, develop a roadmap for technology evolution, discuss its limitations, and provide valuable insights into technological environments. This will help researchers to understand the current options and gaps in this area, thus contributing to this line of research.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Review on Systems-Based Sensory Gloves for Sign Language Recognition State of the Art between 2007 and 2017

Ahmed

Zaidan

et al. 2018

Sensors

188

View full text Add to dashboard Cite

show abstract

“…An approach to evaluate the quality of image registration algorithms was presented in [27]. Besides many applications from the field of quality quantification, a part of research focuses on uncertainty quantification in areas such as face recognition and other biometric technologies [9,10,14], the tracking of shapes in ultrasound images [57] or to evaluate the impact of noisy measurements on the validity of diagnosis results [33]. An uncertainty formulation based on fuzzy set theory has been employed to perform pixel-or object-based classification tasks [11,39,52].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-based propagation of prior knowledge to improve large-scale image analysis pipelines

Stegmaier

Mikut

2017

PLoS ONE

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Many automatically analyzable scientific questions are well-posed and a variety of information about expected outcomes is available a priori. Although often neglected, this prior knowledge can be systematically exploited to make automated analysis operations sensitive to a desired phenomenon or to evaluate extracted content with respect to this prior knowledge. For instance, the performance of processing operators can be greatly enhanced by a more focused detection strategy and by direct information about the ambiguity inherent in the extracted data. We present a new concept that increases the result quality awareness of image analysis operators by estimating and distributing the degree of uncertainty involved in their output based on prior knowledge. This allows the use of simple processing operators that are suitable for analyzing large-scale spatiotemporal (3D+t) microscopy images without compromising result quality. On the foundation of fuzzy set theory, we transform available prior knowledge into a mathematical representation and extensively use it to enhance the result quality of various processing operators. These concepts are illustrated on a typical bioimage analysis pipeline comprised of seed point detection, segmentation, multiview fusion and tracking. The functionality of the proposed approach is further validated on a comprehensive simulated 3D+t benchmark data set that mimics embryonic development and on large-scale light-sheet microscopy data of a zebrafish embryo. The general concept introduced in this contribution represents a new approach to efficiently exploit prior knowledge to improve the result quality of image analysis pipelines. The generality of the concept makes it applicable to practically any field with processing strategies that are arranged as linear pipelines. The automated analysis of terabyte-scale microscopy data will especially benefit from sophisticated and efficient algorithms that enable a quantitative and fast readout.

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“…Rule-basedframeworkhasbeenintroducedasagesturerecognitionmethodusingthejointangles (parameters)obtainedbysensordevices(equipment)inordertocreatesequencesorsetsofrules (ruletypesandmethods).Astheclinicianusesvisualassessmentfortheprognosis,visualequipment arefrequentlyusedfortherule-basedframework.Inparticular,Kinectisusedtoextractthebody segmentorientation (Clark,Pua,Bryant,&Hunt,2013)andjointangles (Bedregal,Costa,&Dimuro, 2006;Hachaj&Ogiela,2014)astheinputstoformrules.However,othertypesofequipmentsuch asInertialSensors (Bo,Hayashibe,&Poignet,2011)androbotics (Akdo˘gan,Taçgın,&Adli,2009) arealsobeenusedtoobtainsensordatatoderivethejointanglesandorientation.…”

Section: Rule-based Approachmentioning

confidence: 99%

Lower-Limb Rehabilitation at Home

Yean

Lee

Yeo

2021

International Journal of Interdisciplinary Telecommunications and Networking

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Ageing causes loss of muscle strength, especially on the lower limbs, resulting in higher risk to injuries during functional activities. The path to recovery is through physiotherapy and adopt customized rehabilitation exercise to assist the patients. Hence, lowering the risk of incorrect exercise at home involves the use of biofeedback for physical rehabilitation patients and quantitative reports for clinical physiotherapy. This research topic has garnered much attention in recent years owing to the fast ageing population and the limited number of clinical experts. In this paper, the authors survey the existing works in exercise assessment and state identification. The evaluation results in the accuracy of 95.83% average classifying exercise motion state using the proposed raw signal. It confirmed that raw signals have more impact than using sensor-fused Euler and joint angles in the state identification prediction model.

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Interval Fuzzy Rule-Based Hand Gesture Recognition

Cited by 17 publications

References 31 publications

A Review on Systems-Based Sensory Gloves for Sign Language Recognition State of the Art between 2007 and 2017

A Review on Systems-Based Sensory Gloves for Sign Language Recognition State of the Art between 2007 and 2017

Fuzzy-based propagation of prior knowledge to improve large-scale image analysis pipelines

Lower-Limb Rehabilitation at Home

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