A web browsing interface for people with severe speech and motor impairment

Purpose The Web has become an indispensable medium used by people across the world for education, information, entertainment, social interaction as well as for various daily activities involving shopping and employment-related tasks. It is therefore becoming increasingly essential that the Web must be accessible to all people to provide equal access and equal opportunity. This is specifically more important for people with various kind of disabilities. Several initiatives such as development of Web accessibility guidelines, tools and technologies have been undertaken to make the Web usable for people with different disabilities. However, only a handful of them are aimed at people with Severe Speech and Motor Impairment (SSMI). This paper aims to present a Web browsing interface for people with severe speech and motor impairment. Design/methodology/approach The browser allows easy dissemination of information through World Wide Web for people with SSMI. The browser is augmented with both automatic as well as manual scanning mechanisms through which a motor disorder person can access the browser graphical user interface (GUI). Further, the browser provides an intelligent content scanning mechanism through which the Web contents can be accessed with less time and cognitive effort. Along with the desktop version, WebSanyog is successfully ported on Android-based tablets to make the system portable. Findings The system has been exhaustively field tested by people with SSMI. The browser has been deployed at the Indian Institute of Cerebral Palsy (IICP), Kolkata. The performance of the browser has been measured in terms of three parameters: The Task execution time (TET); Error rates analysis (ER); and Overall usability score by the subject. The evaluation results suggests that the proposed Web browsing interface is effective in terms of task execution time, cognitive effort and overall user satisfaction. Originality/value The browser GUI is integrated with an automatic scanning mechanism as an alternate way to access and navigate through Web pages, instead of using keyboard and mouse. The browser provides novel content access mechanisms that makes navigating through Web page contents like links, images and embedded videos easier and faster. To facilitate text entry, the browser provides two different options, namely, the predictive virtual scanning keyboard and a novel icon-based query entry scheme that allows generating search queries through the selection of multiple icons.

A Comparison of Methods of Controlling the Movement of an Exoskeleton, Supporting Movements of the Upper Limb Using Signals of Muscle Activity and Manual Controls

The paper presents the research methodology and the analysis of the results of the comparison test of two methods of controlling the exoskeleton of the upper limb using signals of muscle activity and manual control devices. The results show the advantage of the joystick method over EMG in terms of usability, task execution time, ease of use and comfort.

Data-Intensive Task Scheduling for Heterogeneous Big Data Analytics in IoT System

Efficient big data analysis is critical to support applications or services in Internet of Things (IoT) system, especially for the time-intensive services. Hence, the data center may host heterogeneous big data analysis tasks for multiple IoT systems. It is a challenging problem since the data centers usually need to schedule a large number of periodic or online tasks in a short time. In this paper, we investigate the heterogeneous task scheduling problem to reduce the global task execution time, which is also an efficient method to reduce energy consumption for data centers. We establish the task execution for heterogeneous tasks respectively based on the data locality feature, which also indicate the relationship among the tasks, data blocks and servers. We propose a heterogeneous task scheduling algorithm with data migration. The core idea of the algorithm is to maximize the efficiency by comparing the cost between remote task execution and data migration, which could improve the data locality and reduce task execution time. We conduct extensive simulations and the experimental results show that our algorithm has better performance than the traditional methods, and data migration actually works to reduce th overall task execution time. The algorithm also shows acceptable fairness for the heterogeneous tasks.

ANN Based Execution Time Prediction Model and Assessment of Input Parameters through ISM

Cloud computing is on-demand network access model which provides dynamic resource provisioning, selection and scheduling. The performance of these techniques extensively depends on the prediction of various factors e.g., task execution time, resource trust value etc., As the accuracy of prediction model absolutely depends on the input data that are fed into the network, Selection of suitable inputs also plays vital role in predicting the appropriate value. Based on predicted value, Scheduler can choose the suitable resource and perform scheduling for efficient resource utilization and reduced makespan estimates. However, precise prediction of execution time is difficult in cloud environment due to heterogeneous nature of resources and varying input data. As each task has different characteristic and execution criteria, the environment must be intelligent enough to select the suitable resource. To solve these issues, an Artificial Neural Network (ANN) based prediction model is proposed to predict the execution time of tasks. First, input parameters are identified and selected through Interpretive Structural Modeling (ISM) approach. Second, a prediction model is proposed for predicting the task execution time for varying number of inputs. Third, the proposed model is validated and provides 21.72% reduction in mean relative error compared to other state-of-the-art methods

Rectilinear Tasks Optimization of a Modular Serial Metamorphic Manipulator

This paper proposes a method for optimizing rectilinear tasks on a 3 degrees-of-freedom (DoF) modular serial metamorphic manipulator. The overall experimental process was designed in order for theoretical assumptions and previous experimental results, regarding the characteristics of a class of reconfigurable manipulators to be verified. The optimization procedure undergoes two stages. In each stage, the tasks are initially simulated and the optimal solutions obtained are afterward evaluated in the manipulator. Optimal task placement in the configuration space of the reference anatomy is concerned in the first optimization stage. Two different kinematic manipulability measures are utilized to form the objective function of the genetic algorithm (GA) used. Determination of the optimal anatomy for each task execution is concerned in the second stage. All feasible anatomies are exhaustively evaluated, and the anatomy with minimum execution time achieved in simulation is considered as optimal. The simulated tasks are executed for the reference and the optimal anatomy extracted. Overall task execution time reduction is measured. For tasks executed, Tool Center Point (TCP) position and velocity are obtained from navigation equations using measurements from an inertial measurement unit (IMU) sensor. In order to obtain more accurate solutions from position and velocity equations, a Kalman filter (KF) algorithm is implemented. Finally, conclusions are made based on the results of each task execution. Overall the metamorphic manipulator can achieve higher kinematic performance and minimize task execution time for the optimal anatomy calculated. Optimal task placement for the reference anatomy also reduces the task execution time.