Interactive Computer System Research Articles

An efficient interpretation of hand gestures to control smart interactive television

In the recent era of smart world, smarter technologies are gaining focus on human computer interaction (HCI) systems, and traditional ways like remote control, mouse, keyboard, etc. are becoming less popular. This paper presents a framework of simple yet efficient approach for future applications of the new age smart and intelligent technologies that shall enhance the HCI specifically for smart television. Hand gesture recognition (HGR)-based model is proposed for wireless control of smart interactive television (SITV), which includes controlling volume and selecting channels. The proposed framework undergoes three steps: 1) the hand gesture of the person is detected by using shape, colour and skin similarity; 2) the extracted features are classified by using rule-based classification and a gesture code is generated; 3) the classified gesture is interpreted by a novel interpreter. The performance of the proposed framework is evaluated with different people's hand gestures and compared with the techniques of others.

An efficient interpretation of hand gestures to control smart interactive television

In the recent era of smart world, smarter technologies are gaining focus on human computer interaction (HCI) systems, and traditional ways like remote control, mouse, keyboard, etc. are becoming less popular. This paper presents a framework of simple yet efficient approach for future applications of the new age smart and intelligent technologies that shall enhance the HCI specifically for smart television. Hand gesture recognition (HGR)-based model is proposed for wireless control of smart interactive television (SITV), which includes controlling volume and selecting channels. The proposed framework undergoes three steps: 1) the hand gesture of the person is detected by using shape, colour and skin similarity; 2) the extracted features are classified by using rule-based classification and a gesture code is generated; 3) the classified gesture is interpreted by a novel interpreter. The performance of the proposed framework is evaluated with different people's hand gestures and compared with the techniques of others.

The JESTKOD database: an affective multimodal database of dyadic interactions

In human-to-human communication, gesture and speech co-exist in time with a tight synchrony, and gestures are often utilized to complement or to emphasize speech. In human–computer interaction systems, natural, affective and believable use of gestures would be a valuable key component in adopting and emphasizing human-centered aspects. However, natural and affective multimodal data, for studying computational models of gesture and speech, is limited. In this study, we introduce the JESTKOD database, which consists of speech and full-body motion capture data recordings in dyadic interaction setting under agreement and disagreement scenarios. Participants of the dyadic interactions are native Turkish speakers and recordings of each participant are rated in dimensional affect space. We present our multimodal data collection and annotation process, as well as our preliminary experimental studies on agreement/disagreement classification of dyadic interactions using body gesture and speech data. The JESTKOD database provides a valuable asset to investigate gesture and speech towards designing more natural and affective human–computer interaction systems.

A novel Adaptive Fractional Deep Belief Networks for speaker emotion recognition

Due to the rapid development of human computer interaction systems, the recognition of emotion becomes a challenging task. Various handheld devices such as smart phones and PCs are utilized to recognize the human emotion from the speech. But, the recognition of emotion is burdensome to the human computer interaction system since it differs according to the speaker. To resolve this problem, the Adaptive Fractional Deep Belief Network (AFDBN) is proposed in this paper. Initially, the spectral features are extracted from the input speech signal. The features obtained are the tonal power ratio, spectral flux, pitch chroma and MFCC. The extracted feature set is then given into the network for the classification. Thus, the AFDBN is newly designed by the fractional theory and Deep belief network. Then, the proposed AFDBN method is used to find out the optimal weights which are used to recognize the emotion efficiently. Finally, the experimental results are evaluated and its performance is analyzed by the evaluation metrics which is compared with the existing systems. The outcome of the proposed method attains 99.17% accuracy for Berlin database and 97.74% for Telugu database.

A computer-human interaction model to improve the diagnostic accuracy and clinical decision-making during 12-lead electrocardiogram interpretation

IntroductionThe 12-lead Electrocardiogram (ECG) presents a plethora of information and demands extensive knowledge and a high cognitive workload to interpret. Whilst the ECG is an important clinical tool, it is frequently incorrectly interpreted. Even expert clinicians are known to impulsively provide a diagnosis based on their first impression and often miss co-abnormalities. Given it is widely reported that there is a lack of competency in ECG interpretation, it is imperative to optimise the interpretation process. Predominantly the ECG interpretation process remains a paper based approach and whilst computer algorithms are used to assist interpreters by providing printed computerised diagnoses, there are a lack of interactive human-computer interfaces to guide and assist the interpreter. MethodsAn interactive computing system was developed to guide the decision making process of a clinician when interpreting the ECG. The system decomposes the interpretation process into a series of interactive sub-tasks and encourages the clinician to systematically interpret the ECG. We have named this model ‘Interactive Progressive based Interpretation’ (IPI) as the user cannot ‘progress’ unless they complete each sub-task. Using this model, the ECG is segmented into five parts and presented over five user interfaces (1: Rhythm interpretation, 2: Interpretation of the P-wave morphology, 3: Limb lead interpretation, 4: QRS morphology interpretation with chest lead and rhythm strip presentation and 5: Final review of 12-lead ECG). The IPI model was implemented using emerging web technologies (i.e. HTML5, CSS3, AJAX, PHP and MySQL). It was hypothesised that this system would reduce the number of interpretation errors and increase diagnostic accuracy in ECG interpreters. To test this, we compared the diagnostic accuracy of clinicians when they used the standard approach (control cohort) with clinicians who interpreted the same ECGs using the IPI approach (IPI cohort). ResultsFor the control cohort, the (mean; standard deviation; confidence interval) of the ECG interpretation accuracy was (45.45%; SD=18.1%; CI=42.07, 48.83). The mean ECG interpretation accuracy rate for the IPI cohort was 58.85% (SD=42.4%; CI=49.12, 68.58), which indicates a positive mean difference of 13.4%. (CI=4.45, 22.35) An N−1 Chi-square test of independence indicated a 92% chance that the IPI cohort will have a higher accuracy rate. Interpreter self-rated confidence also increased between cohorts from a mean of 4.9/10 in the control cohort to 6.8/10 in the IPI cohort (p=0.06). Whilst the IPI cohort had greater diagnostic accuracy, the duration of ECG interpretation was six times longer when compared to the control cohort. ConclusionsWe have developed a system that segments and presents the ECG across five graphical user interfaces. Results indicate that this approach improves diagnostic accuracy but with the expense of time, which is a valuable resource in medical practice.

Introduction to Special Issue: Foundations of Cognitive Science for the Design of Human–Computer Interactive Systems

The primary goal of this special issue is to provide an overview of select topics in cognitive science and their applications to the design of interactive systems. Authors were requested to show ho...

Design, implementation, and verification of computer interactive parallel connection system with load-sharing control for synchronous generators

This study presents an advanced computer interactive system for automatic parallel connection of synchronous generators and load sharing control between them. Because of its high operation speed and precision, a dsPIC microcontroller has been used in the study to control the conditions for paralleling and then to realize the connection, to transfer the real time data among the control computer and other environmental units as long as the system operates. Thus, it has been possible to monitor all parameters of the system in real time on a user friendly computer interface. Furthermore, active and reactive load sharing ratios of the generators can be easily set using the same interface. The system has been tested experimentally, and quite satisfactory results have been obtained. Hence, it can be used in energy generation centers safely. Furthermore, the system is quite convenient for the next generation smart grid infrastructure.

Classification of EEG Signal using Correlation Coefficient among Channels as Features Extraction Method

Objectives: In this paper, we have evaluated the effectiveness of classification of Electroencephalogram (EEG) signals using the correlation between channels as a method of features selection. Methods/Statistical Analysis: First data is broken sample wise, then correlation coefficient between channel pair for each sample is calculated. After that mean of the correlation coefficient of all channel pair for each class over all samples is calculated and in a similar manner, standard deviation from the mean is also calculated. For feature selection we have plotted a pair of the Gaussian curves between channels of two separate classes and choose those channels which give us lower misclassified area as features. Then these features are used for training purpose of Support Vector Machine (SVM). Findings: Most of the previous researches follow either signal processing approach or machine learning approach while we emphasized upon the nature of the signal propagation amongst the neurons. The basic idea behind the feature selection is taken from the way the signals propagate from one neuron to the other. In our work we assume that EEG signals follow the normal distribution and verify the fact using chi-square test. On applying SVM the accuracy of classification on testing data confirms that correlation among channels can be used for feature selection. Application/Improvements: The results can be improved by improving the pre-processing of EEG signals. It can be used to develop a Brain Computer Interaction (BCI) system.

Temporal Pyramid Matching of Local Binary Subpatterns for Hand-Gesture Recognition

Human–computer Interaction systems based on hand-gesture recognition are nowadays of great interest to establish a natural communication between humans and machines. However, the visual recognition of gestures and other human poses remains a challenging problem. In this paper, the original volumetric spatiograms of local binary patterns descriptor has been extended to efficiently and robustly encode the spatial and temporal information of hand gestures. This enhancement mitigates the dimensionality problems of the previous approach, and considers more temporal information to achieve a higher recognition rate. Excellent results have been obtained, outperforming other existing approaches of the state of the art.

WiGeR: WiFi-Based Gesture Recognition System

Recently, researchers around the world have been striving to develop and modernize human–computer interaction systems by exploiting advances in modern communication systems. The priority in this field involves exploiting radio signals so human–computer interaction will require neither special devices nor vision-based technology. In this context, hand gesture recognition is one of the most important issues in human–computer interfaces. In this paper, we present a novel device-free WiFi-based gesture recognition system (WiGeR) by leveraging the fluctuations in the channel state information (CSI) of WiFi signals caused by hand motions. We extract CSI from any common WiFi router and then filter out the noise to obtain the CSI fluctuation trends generated by hand motions. We design a novel and agile segmentation and windowing algorithm based on wavelet analysis and short-time energy to reveal the specific pattern associated with each hand gesture and detect duration of the hand motion. Furthermore, we design a fast dynamic time warping algorithm to classify our system’s proposed hand gestures. We implement and test our system through experiments involving various scenarios. The results show that WiGeR can classify gestures with high accuracy, even in scenarios where the signal passes through multiple walls.

Interactive 3D object recognition pipeline on mobile GPGPU computing platforms using low-cost RGB-D sensors

In this work, we propose the implementation of a 3D object recognition system which will be optimized to operate under demanding time constraints. The system must be robust so that objects can be recognized properly in poor light conditions and cluttered scenes with significant levels of occlusion. An important requirement must be met: The system must exhibit a reasonable performance running on a low power consumption mobile GPU computing platform (NVIDIA Jetson TK1) so that it can be integrated in mobile robotics systems, ambient intelligence or ambient-assisted living applications. The acquisition system is based on the use of color and depth (RGB-D) data streams provided by low-cost 3D sensors like Microsoft Kinect or PrimeSense Carmine. The resulting system is able to recognize objects in a scene in less than 7 seconds, offering an interactive frame rate and thus allowing its deployment on a mobile robotic platform. Because of that, the system has many possible applications, ranging from mobile robot navigation and semantic scene labeling to human–computer interaction systems based on visual information. A video showing the proposed system while performing online object recognition in various scenes is available on our project website ( http://www.dtic.ua.es/~agarcia/3dobjrecog-jetsontk1/ ).

Robust Fingertip Detection in a Complex Environment

Fingertip detection has a broad application in gesture recognition and finger tracking. It is also an important foundation of human–computer interaction systems. However, most algorithms are suitable for simple conditions with low accuracy because the hand is a nonrigid object, and its appearance model is complex. To address the challenging problem of accurately detecting fingertips in a complex environment, we propose a novel and robust fingertip detection algorithm in this paper. Unlike existing methods, our study requires no special device or mark, and users are free to move their hands. Via dense optical flow and a skin filter, we perform complete hand region segmentation in a complex environment. We find the maximum value of the local centroid distance outside the centroid circles and identify fingertips. Our algorithm performs favorably compared with common hand region segmentation and fingertip detection methods. Thorough experimentation proves that our proposed algorithm is effective and robust.

A Calibration Method for Blink Type Classification

SUMMARYHuman–computer interaction systems that use eye blinks have been reported. One purpose of these systems is as a communication aid for persons with severe physical disabilities such as amyotrophic lateral sclerosis (ALS). Some of these systems classify the user's voluntary (conscious) blinks. If the voluntary blinks can be distinguished automatically, the usability of the input interface will be improved. We have proposed an eye blink extraction method that utilizes split interlaced images of the eye. These split images are odd‐ and even‐field images and are generated from interlaced images. The proposed method yields a time resolution that is double that of the 1080i Hi‐Vision format. We refer to this approach as a “frame splitting method”. We realized a method for automatic eye blink extraction using the frame splitting method. Based on this extraction method, we also proposed a blink type classification method in which a threshold is set within the duration of an eye blink. Our classification method requires a calibration process before measurement. In this paper, we clarify the required minimum number of eye blinks for calibration. We also discuss the performance of blink type classification using this calibration.

Computational cognitive assistants for futures studies: Toward vision based simulation

Many foresight researchers believe that quantitative simulations have a very restricted contribution in futures studies due to their simplicity and lack of creativity. While qualitative methods, taking advantage of the human cognitive system, have a great potential in addressing a wide range of problems in futures studies, this potential is mostly due to the human visual logic that can handle the task of imagining future scenarios much better than mathematical logic.On the other hand, computational methods benefit from the advantages of silicon-based systems namely speed, large memory, rapid networking, and communication. Hence, it would be extremely beneficial to come up with a solution that combines the positive sides of both qualitative and computational approaches. Cognitive artificial agents are computational units that make use of the human cognitive system. Their interaction with foresight and futures researchers can result in promising solutions for the problems addressed in futures studies. In addition, these agents can serve as a great source of inspiration for taking the first step towards vision based computers that can simulate humans’ imaginations of the future.This paper reviews some of the previous attempts in this field and finally sheds light on the main issues where methods in futures studies can play a key role in the future of Human Computer Interaction systems. Our suggested architecture for a future studies interactions-based system along with its justifications and specifications is provided in the form of a request for proposal.

A novel approach to improve the planning of adaptive and interactive sessions for the treatment of Major Depression

Human Computer Interaction (HCI) is a research field which aims to improve the relationship between users and interactive computer systems. A main objective of this research area is to make the user experience more pleasant and efficient, minimizing the barrier between the users' cognition of what they want to accomplish and the computer's understanding of the user's tasks, by means of user-friendly, useful and usable designs. A bad HCI design is one of the main reasons behind user rejection of computer-based applications, which in turn produces loss of productivity and economy in industrial environments.In the eHealth domain, user rejection of computer-based systems is a major barrier to exploiting the maximum benefit from those applications developed to support the treatment of diseases, and in the worst cases a poor design in these systems may cause deterioration in the clinical condition of the patient. Thus, a high level of personalisation of the system according to users' needs is extremely important, making it easy to use and contributing to the system's efficacy, which in turn facilitates the empowerment of the target users. Ideally, the content offered through the interactive sessions in these applications should be continuously assessed and adapted to the changing condition of the patient. A good HCI design and development can improve the acceptance of these applications and contribute to promoting better adherence levels to the treatment, preventing the patient from further relapses.In this work, we present a mechanism to provide personalised and adaptive daily interactive sessions focused on the treatment of patients with Major Depression. These sessions are able to automatically adapt the content and length of the sessions to obtain personalised and varied sessions in order to encourage the continuous and long-term use of the system. The tailored adaptation of session content is supported by decision-making processes based on: (i) clinical requirements; (ii) the patient's historical data; and (iii) current responses from the patient. We have evaluated our system through two different methodologies: the first one performing a set of simulations producing different sessions from changing input conditions, in order to assess different levels of adaptability and variability of the session content offered by the system. The second evaluation process involved a set of patients who used the system for 14-28 days and answered a questionnaire to provide feedback about the perceived level of adaptability and variability produced by the system. The obtained results in both evaluations indicated good levels of adaptability and variability in the content of the sessions according to the input conditions. We present an HCI-based framework for the treatment of patients with Major Depression.The framework provides adaptive and personalised plans of therapeutic-based sessions.The Intelligent framework reacts according to the individual condition of the patient.Its functionality was evaluated using a relevant set of simulations and real patients.The developed framework can be adapted as a CDSS addressing mental health disorders.

Planning the supply of gas LPG to the filling station network in the concept of stocks controlled by the carrier

In this paper, supply system of LPG to fuel station is presented as a specific example of vendor managed inventory replenishment concept. A single hauler of LPG is considered, which operates within the network of domestic depots and performs deliveries to hundreds of final customers being responsible for full accessibility for all supported LPG stations. The carrier is responsible for the entire LPG supply planning process (volume as well as scheduling of deliveries), information process, completed deliveries and any problems arising during execution. In the article the LPG supply process and the planning system which has been developed are presented with a brief presentation of an interactive computer system (DSS implementation) developed and implemented in the reporting enterprise. The work ends with an indication of the benefits of using a system approach to planning and determination of the directions for further research.

Development and testing of an automated computer tablet-based method for self-testing of high and low contrast near visual acuity in ophthalmic patients

Many eye diseases require on-going assessment for optimal management, creating an ever-increasing burden on patients and hospitals that could potentially be reduced through home vision monitoring. However, there is limited evidence for the utility of current applications and devices for this. To address this, we present a new automated, computer tablet-based method for self-testing near visual acuity (VA) for both high and low contrast targets. We report on its reliability and agreement with gold standard measures. The Mobile Assessment of Vision by intERactIve Computer (MAVERIC) system consists of a calibrated computer tablet housed in a bespoke viewing chamber. Purpose-built software automatically elicits touch-screen responses from subjects to measure their near VA for either low or high contrast acuity. Near high contrast acuity was measured using both the MAVERIC system and a near Landolt C chart in one eye for 81 patients and low contrast acuity using the MAVERIC system and a 25% contrast near EDTRS chart in one eye of a separate 95 patients. The MAVERIC near acuity was also retested after 20min to evaluate repeatability. Repeatability of both high and low contrast MAVERIC acuity measures, and their agreement with the chart tests, was assessed using the Bland-Altman comparison method. One hundred and seventy-three patients (96%) completed the self- testing MAVERIC system without formal assistance. The resulting MAVERIC vision demonstrated good repeatability and good agreement with the gold-standard near chart measures. This study demonstrates the potential utility of the MAVERIC system for patients with ophthalmic disease to self-test their high and low contrast VA. The technique has a high degree of reliability and agreement with gold standard chart based measurements.

On the sojourn time distribution in a finite population Markovian processor sharing queue

We consider a finite population processor sharing (PS) queue, with Markovian arrivals and an exponential server. Such a queue can model an interactive computer system consisting of a bank of terminals in series with a central processing unit. For systems with a large population N and a commensurately rapid service rate, or infrequent arrivals, we obtain various asymptotic results. We analyse the conditional sojourn time distribution of a tagged customer, conditioned on the number n of others in the system at the tagged customer's arrival instant, and also the unconditional distribution. The asymptotics are obtained by a combination of singular perturbation methods and spectral methods.We consider several space/time scales and parameter ranges, which lead to different asymptotic behaviours. We also identify precisely when the finite population model can be approximated by the standard infinite population M/M/1 - PS queue.

An interactive system for original necklace design

We propose an interactive system to assist novices in the design and construction of original necklaces. The system consists of two design tools, an interactive drag-and-drop design tool using images of pearls and a design selection tool with an interactive evolutionary computation (IEC) system. The system includes a virtual modeling simulation which allows users to superimpose a necklace design over their own photograph, taken with a web camera. The system also provides a customized construction guide to assist the user with the construction process. We conduct a field trial to demonstrate that non-professional users can design original necklaces using our system.

USE OF DIDACTIC OPPORTUNITIES OF METHODS OF SORTING

The work purpose is not only to implement a computer interactive learning system methods of sort, visualize sorting methods, but to demonstrate the impact of appropriately chosen data structures in the computational process. After all, according to the well-known formula N. Wirth: Algorithms + data Structures = Programs. Sorting is a good method of showing how the goal can be achieved by different methods (algorithms), each with their advantages and disadvantages, assessed from the point of view of the cash inputs and requirements. To achieve the didactic purposes we use the Delphi environment to ensure the efficient work of the programmers, with the involvement of the full potential of Windows. Multi-window capabilities of the medium, ease of coding programs make possible implementation of an interactive learning system sorting numbers. Analyze performance indicators of the method of sorting is the number of comparisons and permutations. They are the basic operations performed on numeric vectors. You should determine an effective and relevant method of sorting according to these two indicators. Really, the probability of operating with a large dataset – more than the small, should, therefore identify the most effective method for computer resources (speed sorting and memory use). For each method, the user receives the necessary information on the sorting method, the system then sorts the sequence of steps interactively with visualization. A comparative analysis of methods of sorting, the results of the analysis to identify an effective method of sorting.