Human Computer Interaction Using Hand Gestures

Premaratne, Prashan; Nguyen, Quang; Premaratne, Malin

doi:10.1007/978-3-642-14831-6_51

Cited by 22 publications

(36 citation statements)

References 13 publications

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“…The model was tested with test data obtained from UCI machine learning repository [23]. The result produced by the system was compared with that produced with WEKA.…”

Section: Resultsmentioning

confidence: 99%

Predicting the Risk of Infection with SCHISTOSOMA HAEMATOBIUM using Machine Learning

Oluwatosin¹,

Angela²

2016

IJCA

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Most developing countries of the world, particularly in subsaharan Africa, exhibit high levels of morbidity and mortality associated with the disease Schistosomiasis caused by the parasite Schistosoma Haematobium. Most individuals at risk of Schistosomiasis reside between latitudes 36°N and 34°S where average fresh water temperatures range from 25° to 30°C, placing African states among the most affected countries. Schistosoma-Mansoni and Schistosoma Haematobium account for most Schistosoma species infection in Africa. In this research work, a computational approach was provided to estimate the prevalence of Schistosoma Haematobium using machine learning to predict if an individual is infected with Schistosomiasis as well as classify individuals into groups to determine their risk of infection. The Neural Network machine learning approach was used which involves the use of supervised learning method in which for every set of input, there is an expected output or a set of expected outputs. The algorithm used for the model is almost the same as that used for a neural network single layer perceptron but with little variations in the transformation function applied on the inputs. The algorithm, which was implemented with Java programming language, involves assigning a weight or degree of importance to each input, finding the weighted sum of those input values, applying a transformation function on the weighted sum and adjusting the weight of each input. The inputs supplied to this model are the age, sex and location of a person.

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“…The model was tested with test data obtained from UCI machine learning repository [23]. The result produced by the system was compared with that produced with WEKA.…”

Section: Resultsmentioning

confidence: 99%

Predicting the Risk of Infection with SCHISTOSOMA HAEMATOBIUM using Machine Learning

Oluwatosin¹,

Angela²

2016

IJCA

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“…Therefore, in addition to developing an HCI environment, the system provides a natural way to interact freely on a scenario through a data glove. Mainly, the system focus on the use of natural movements and gestural protocols (in HCI, set of gestures to establish communication between human and system) performed by hand [22,23]. As an effective way to prove how positive can be this kind of interaction, it is worth to perform a user's experiment that shows the richness of the virtual experience.…”

Section: Related Workmentioning

confidence: 99%

Gestural Interaction for Virtual Reality Environments through Data Gloves

Rodríguez¹,

Jofré²,

Alvarado³

et al. 2017

Adv. sci. technol. eng. syst. j.

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“…There are significant obstacles that has stagnated the accuracy of hand gesture recognition which has confined the number of gestures suitable for communication to very few (less than 30) due to the difficulty in accurately recognizing hand gestures by different users. There are few obvious reasons behind the failure of many vision based classification systems to interpret sign languages over the years [2]. Some of the more prominent ones can be listed as follows:…”

Section: Introductionmentioning

confidence: 99%

“…There has been significant success in controlling many electronic apparatus using hand gestures in the past [1]. HCI has featured prominently in most consumer-electronics control systems over the past decade [2].…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, this will allow normal users to communicate with machines at speeds exceeding that is available with mouse and keyboards as well as seamless integration of entertainment and gaming experience unlike anything that they have experienced before. The work described here has stemmed from the principal author's latest book on 'Human Computer Interaction using Hand Gestures' highlights the current drawbacks associated with HCI and fundamental research that it envisions [2]. As was outlined in the Introduction and Previous Work, existing approaches for gesture recognition has stagnated the sign language recognition beyond a limited number of gestures with significant accuracy.…”

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confidence: 96%

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Dynamic Hand Gesture Recognition Framework

Premaratne

Yang

Zhou³

et al. 2014

Intelligent Computing Methodologies

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Abstract. Sign languages originated long before any speech-based languages evolved in the world. They contain subtleties that rival any speech-based languages conveying a rich source of information much faster than any speechbased languages. Similar to the diversity of speech-based languages, sign languages vary from region to region. However, unlike the speech counterpart, sign languages from diverse regions from the world have much common traits that originate from human evolution. Researchers have been intrigued by these common traits and have always wondered whether sign language-type communication is possible for instructing the computers opposed to the mundane keyboard and mouse. This trend is popularly known as Human Computer Interaction (HCI) and has used a subset of common sign language hand gestures to interact with machines through computer vision.Since the sign languages comprise of thousands of subtle gestures, a new sophisticated approach has to be initiated for eventual recognition of vast number of gestures. Hand gestures comprise of both static postures and dynamic gestures and can carry significantly rich vocabulary describing words in the thousands. In this article, we present our latest research that describes a mechanism to accurately interpret dynamic hand gestures using a concept known as 'gesture primitives' where each dynamic gesture is described as a collection of many primitives over time that can drive a classification strategy based on Hidden Markov Model to reliably predict the gesture using statistical knowledge of such gestures. We believe that even though our work is in its infancy, this strategy can be extended to thousands of dynamic gestures used in sign language to be interpreted by machines.

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Human Computer Interaction Using Hand Gestures

Cited by 22 publications

References 13 publications

Predicting the Risk of Infection with SCHISTOSOMA HAEMATOBIUM using Machine Learning

Predicting the Risk of Infection with SCHISTOSOMA HAEMATOBIUM using Machine Learning

Gestural Interaction for Virtual Reality Environments through Data Gloves

Dynamic Hand Gesture Recognition Framework

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