Current Issue : January - March Volume : 2019 Issue Number : 1 Articles : 5 Articles
Some disadvantages of optical eye tracking systems have increased the interest to EOG (Electrooculography) based Human\nComputer Interaction (HCI). However, text entry attempts using EOG have been slower than expected because the eyes should\nmove several times for entering a character. In order to improve the writing speed and accuracy of EOG based text entry, a new\nmethod based on the coding of eye movements has been suggested in this study. In addition, a real time EOG based HCI system\nhas developed to implement the method. In our method all characters have been encoded by single saccades in 8 directions and\ndifferent dwell time. In order to standardize dwell times and facilitate the coding process, computer assisted voice guidance was\nused. A number of experiments have been conducted to examine the effectiveness of the proposed method and system. At the\nend of the fifth trials, an experienced user was able to write at average 13.2 wpm (5 letters = 1 word) with 100% accuracy using the\ndeveloped system. The results of our experiments have shown that text entry with the eye can be done quickly and efficiently with\nthe proposed method and system....
Designmimetics is an importantmethod of creation in technology design.Here, we review designmimetics as a plausible approach\nto address the problem of howto design generally intelligent technology.We argue that designmimetics can be conceptually divided\ninto three levels based on the source of imitation. Biomimetics focuses on the structural similarities between systems in nature and\ntechnical solutions for solving design problems. In robotics, the sensory-motor systems of humans and animals are a source of\ndesign solutions. At the highest level, we introduce the concept of cognitive mimetics, in which the source for imitation is human\ninformation processing. We review and discuss some historical examples of cognitive mimetics, its potential uses, methods, levels,\nand current applications, and how to test its success.We conclude by a practical example showing how cognitive mimetics can be\na highly valuable complimentary approach for pattern matching and machine learning based design of artificial intelligence (AI)\nfor solving specific human-AI interaction design problems....
Brain-Computer Interface (BCI) is a rapidly developing technology that aims to support individuals suffering from various\ndisabilities and, ultimately, improve everyday quality of life. Sensorimotor rhythm-based BCIs have demonstrated remarkable\nresults in controlling virtual or physical external devices but they still face a number of challenges and limitations. Main challenges\ninclude multiple degrees-of-freedom control, accuracy, and robustness. In this work, we develop a multiclass BCI decoding\nalgorithm that uses electroencephalography (EEG) source imaging, a technique that maps scalp potentials to cortical activations,\nto compensate for low spatial resolution of EEG. Spatial features were extracted using Common Spatial Pattern (CSP) filters in the\ncortical source space from a number of selected Regions of Interest (ROIs). Classification was performed through an ensemble\nmodel, based on individual ROI classification models. The evaluation was performed on the BCI Competition IV dataset 2a, which\nfeatures 4 motor imagery classes from 9 participants. Our results revealed a mean accuracy increase of 5.6% with respect to the\nconventional application method of CSP on sensors. Neuroanatomical constraints and prior neurophysiological knowledge play\nan important role in developing source space-based BCI algorithms. Feature selection and classifier characteristics of our\nimplementation will be explored to raise performance to current state-of-the-art....
This paper reports on an empirical study that compares two sets of heuristics, Nielsenâ??s heuristics and the SMART heuristics in the\nidentification of usability problems in a mobile guide smartphone app for a living museum. Five experts used the severity rating\nscales to identify and determine the severity of the usability issues based on the two sets of usability heuristics.The study found\nthat Nielsenâ??s heuristics set is too general to detect usability problems in a mobile application compared to SMART heuristics which\nfocuses on the smartphone application in the product development life cycle instead of the generic Nielsenâ??s heuristics which focuses\non a wide range of interactive system. The study highlights the importance of utilizing domain specific usability heuristics in the\nevaluation process. This ensures that relevant usability issues were successfully identified which could then be given immediate\nattention to ensure optimal user experience....
Background: For the functional control of prosthetic hand, it is insufficient to obtain\nonly the motion pattern information. As far as practicality is concerned, the control of\nthe prosthetic hand force is indispensable. The application value of prosthetic hand will\nbe greatly improved if the stable grip of prosthetic hand can be achieved. To address\nthis problem, in this study, a bio-signal control method for grasping control of a prosthetic\nhand is proposed to improve patientâ??s sense of using prosthetic hand and the\nthus improving the quality of life.\nMethods: A MYO gesture control armband is used to collect the surface electromyographic\n(sEMG) signals from the upper limb. The overlapping sliding window scheme\nare applied for data segmentation and the correlated features are extracted from each\nsegmented data. Principal component analysis (PCA) methods are then deployed for\ndimension reduction. Deep neural network is used to generate sEMG-force regression\nmodel for force prediction at different levels. The predicted force values are input to\na fuzzy controller for the grasping control of a prosthetic hand. A vibration feedback\ndevice is used to feed grasping force value back to patientâ??s arm to improve patientâ??s\nsense of using prosthetic hand and realize accurate grasping. To test the effectiveness\nof the scheme, 15 able-bodied subjects participated in the experiments.\nResults: The classification results indicated that 8-channel sEMG applying all four\ntime-domain features, with PCA reduction from 32 to 8 dimensions results in the highest\nclassification accuracy. Based on the experimental results from 15 participants, the\naverage recognition rate is over 95%. On the other hand, from the statistical results of\nstandard deviation, the between-subject variations ranges from 3.58 to 1.25%, proving\nthat the robustness and stability of the proposed approach.\nConclusions: The method proposed hereto control grasping power through the\npatientâ??s own sEMG signal, which achieves a high recognition rate to improve the\nsuccess rate of grip and increases the sense of operation and also brings the gospel for\nupper extremity amputation patients....
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