Vision-based hand wheel-chair control

Several studies have shown that people with disabilities benefit substantially from access to a means of independent mobility and assistive technology. Researchers are using technology originally developed for mobile robots to create easier to use wheelchairs. With this kind of technology people with disabilities can gain a degree of independence in performing daily life activities. In this work a computer vision system is presented, able to drive a wheelchair with a minimum number of finger commands. The user hand is detected and segmented with the use of a kinect camera, and fingertips are extracted from depth information, and used as wheelchair commands

Evidence on the effect of Claw-Back provisions on IPO share allocation and underpricing in Hong Kong

The article examines share allocation practices of over 300 initial public offerings (IPOs) in Hong Kong during the years immediately following the enacting of a ‘Claw-Back’ provision for IPO share reallocation. The examination of exhaustive micro-level data reveals that small (uninformed, retail) investors earn higher initial returns than large investors. Before the enacting of the ‘Claw-Back’ provision, small investors were unfavourably treated in relation to large investors. The pattern now prevailing in the proportion of shares allocated to small and large investors also differs from that observed previously. When attempting to isolate the determinants of IPO underpricing in Hong Kong, the article also shows that both the ‘informed demand’ hypothesis and the signalling effect of underwriters’ reputation are significant determinants of underpricing. Such result, not visible when pooled OLS regressions are used, becomes apparent through the use of a system of simultaneous equations.info:eu-repo/semantics/acceptedVersio

Generic system for human-computer gesture interaction

Hand gestures are a powerful way for human communication, with lots of potential applications in the area of human computer interaction. Vision-based hand gesture recognition techniques have many proven advantages compared with traditional devices, giving users a simpler and more natural way to communicate with electronic devices. This work proposes a generic system architecture based in computer vision and machine learning, able to be used with any interface for humancomputer interaction. The proposed solution is mainly composed of three modules: a pre-processing and hand segmentation module, a static gesture interface module and a dynamic gesture interface module. The experiments showed that the core of vision-based interaction systems can be the same for all applications and thus facilitate the implementation. In order to test the proposed solutions, three prototypes were implemented. For hand posture recognition, a SVM model was trained and used, able to achieve a final accuracy of 99.4%. For dynamic gestures, an HMM model was trained for each gesture that the system could recognize with a final average accuracy of 93.7%. The proposed solution as the advantage of being generic enough with the trained models able to work in real-time, allowing its application in a wide range of human-machine applications.(undefined

Vision-based hand segmentation techniques for human-robot interaction for real-time applications

One of the most important tasks in hand recognition applications for human-robot interaction is hand segmen-tation. This work presents a method that uses a Microsoft Kinect camera, for hand localization and segmenta-tion. One important aspect for robot control besides hand localization is hand orientation, which is used in this work to control robot heading direction (left or right) and linear velocity. The system first calculates hand po-sition, and then a kalman filter is used to estimate displacement and linear velocity in a smoother way. Ex-perimental results show that the system is easy to use, and can be applied on several different human-computer interface applications

A comparative study of different image features for hand gesture machine learning

Vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition. Hand gesture recognition for human computer interaction is an area of active research in computer vision and machine learning. The primary goal of gesture recognition research is to create a system, which can identify specific human gestures and use them to convey information or for device control. In this paper we present a comparative study of seven different algorithms for hand feature extraction, for static hand gesture classification, analysed with RapidMiner in order to find the best learner. We defined our own gesture vocabulary, with 10 gestures, and we have recorded videos from 20 persons performing the gestures for later processing. Our goal in the present study is to learn features that, isolated, respond better in various situations in human-computer interaction. Results show that the radial signature and the centroid distance are the features that when used separately obtain better results, being at the same time simple in terms of computational complexity.(undefined

A comparison of machine learning algorithms applied to hand gesture recognition

Hand gesture recognition for human computer interaction is an area of active research in computer vision and machine learning. The primary goal of gesture recognition research is to create a system, which can identify specific human gestures and use them to convey information or for device control. This paper presents a comparative study of four classification algorithms for static hand gesture classification using two different hand features data sets. The approach used consists in identifying hand pixels in each frame, extract features and use those features to recognize a specific hand pose. The results obtained proved that the ANN had a very good performance and that the feature selection and data preparation is an important phase in the all process, when using lowresolution images like the ones obtained with the camera in the current work.The authors wish to thank all members of the Laboratorio de Automacao e Robotica, at University of Minho, Guimaraes. Also special thanks to the ALGORITMI Research Centre for the opportunity to develop this research work

Hand gesture recognition for human computer interaction: a comparative study of different image features

Hand gesture recognition for human computer interaction, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. For that, vision-based hand gesture interfaces require fast and extremely robust hand detection, and gesture recognition in real time. In this study we try to identify hand features that, isolated, respond better in various situations in human-computer interaction. The extracted features are used to train a set of classifiers with the help of RapidMiner in order to find the best learner. A dataset with our own gesture vocabulary consisted of 10 gestures, recorded from 20 users was created for later processing. Experimental results show that the radial signature and the centroid distance are the features that when used separately obtain better results, with an accuracy of 91% and 90,1% respectively obtained with a Neural Network classifier. These to methods have also the advantage of being simple in terms of computational complexity, which make them good candidates for real-time hand gesture recognition

Vision-based gesture recognition system for human-computer interaction

Hand gesture recognition, being a natural way of human computer interaction, is an area of active research in computer vision and machine learning. This is an area with many different possible applications, giving users a simpler and more natural way to communicate with robots/systems interfaces, without the need for extra devices. So, the primary goal of gesture recognition research is to create systems, which can identify specific human gestures and use them to convey information or for device control. This work intends to study and implement a solution, generic enough, able to interpret user commands, composed of a set of dynamic and static gestures, and use those solutions to build an application able to work in a realtime human-computer interaction systems. The proposed solution is composed of two modules controlled by a FSM (Finite State Machine): a real time hand tracking and feature extraction system, supported by a SVM (Support Vector Machine) model for static hand posture classification and a set of HMMs (Hidden Markov Models) for dynamic single stroke hand gesture recognition. The experimental results showed that the system works very reliably, being able to recognize the set of defined commands in real-time. The SVM model for hand posture classification, trained with the selected hand features, achieved an accuracy of 99,2%. The proposed solution as the advantage of being computationally simple to train and use, and at the same time generic enough, allowing its application in any robot/system command interface

A decision support system for IST academic information

This article describes the Decision Support System (DSS) for Academic Information being developed at Instituto Superior Técnico, the Engineering School of the Technical University of Lisbon. In Portuguese, this project has been given the acronym SADIA (Sistema de Apoio à Decisão da Informação Académica). This paper focuses on the early phases of the DSS development process, i.e., the business requirements definition and the dimensional modelling. First, we show how the business requirements of the School drive the definition of the DSS dimensional model. Second, we detail the logical dimensional model for a selected business process, the IST Student Admission process. Third, the corresponding physical design decisions are reported. The results obtained from the three phases were successfully validated by business users

Deformidade em supinação na paralisia obstétrica do plexo braquial - Resultados do procedimento de Zancolli

Objetivo: a deformidade em supinação por paralisia obstétrica do plexo braquial (POPB) é atualmente rara e resulta de um desequilíbrio muscular entre pronadores e supinadores. A deformidade é progressiva e disfuncional e, quando a redução passiva é possível, o redireccionamento lateral do tendão distal do bíceps está indicado. Na deformidade fixa do antebraço a membrana interóssea deve ser libertada. Este estudo avalia os resultados do procedimento de Zancolli em doentes com POPB.Doentes e métodos: seis doentes com POPB foram submetidos ao procedimento de Zancolli associado à libertação da membrana interóssea e imobilização pós-operatória por 4 semanas. A idade média foi de 4 anos, com 3.3 anos de seguimento.Resultados: a pronação ativa melhorou em média 70° (50°-90°). Clinicamente, verifica-se melhoria funcional global do membro superior. A única recidiva foi erradamente indicada para o procedimento por insuficiência do bíceps. Comparativamente com estudos prévios com a mesma técnica, a idade de intervenção é baixa e o ganho de pronação ativa elevado. Relativamente a outras técnicas cirúrgicas, a recidiva é menor e não se registam complicações major. Os pais estão satisfeitos.Conclusão: o procedimento de Zancolli pode prevenir deformidade óssea ou luxação da cabeça radial, por isso, em fase precoce esta técnica associa-se a melhores resultados funcionais do que procedimentos cirúrgicos em fase mais avançada. A limitação funcional e deformidade estética da contractura em supinação na POPB são importantes e a técnica de Zancolli apresenta bons resultados