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For the robot-assisted upper limb rehabilitation training process of the elderly with damaged neuromuscular channels and hemiplegic patients, bioelectric signals are added to transform the traditional passive training mode into the active training mode.

This paper mainly builds a steady-state visual stimulation interface, an electroencephalography (EEG) signal processing platform and an exoskeleton robot verification platform. The target flashing stimulation blocks provide visual stimulation at the specified position according to the specified frequency and stimulate EEG signals of different frequency bands. The EEG signal-processing platform constructed in this paper removes the noise by using Butterworth band-pass filtering and common average reference filtering on the obtained signals. Further, the features are extracted to identify the volunteer’s active movement intention through the canonical correlation analysis (CCA) method. The classification results are transmitted to the upper limb exoskeleton robot control system, combined with the position and posture of the exoskeleton robot to control the joint motion of robot.

Through a large number of experimental studies, the average accuracy of offline recognition of motion intention recognition can reach 86.1%. The control strategy with a three-instruction judgment method reduces the average execution error rate of the entire control system to 6.75%. Online experiments verify the feasibility of the steady-state visual evoked potentials (SSVEP)-based rehabilitation system.

An EEG signal analysis method based on SSVEP is integrated into the control of an upper limb exoskeleton robot, transforming the traditional passive training mode into the active training mode. The device used to record EEG is of very low cost, which has the potential to promote the rehabilitation system for further widely applications.

Steady-state visual evoked potential (SSVEP) has been widely used in the application of electroencephalogram (EEG) based non-invasive brain computer interface (BCI) due to its characteristics of high accuracy and information transfer rate (ITR). To recognize the SSVEP components in collected EEG trials, a lot of recognition algorithms based on template matching of training trials have been proposed and applied in recent years. In this paper, a comparative survey of SSVEP recognition algorithms based on template matching of training trails has been done.

To survey and compare the recently proposed recognition algorithms for SSVEP, this paper regarded the conventional canonical correlated analysis (CCA) as the baseline, and selected individual template CCA (ITCCA), multi-set CCA (MsetCCA), task related component analysis (TRCA), latent common source extraction (LCSE) and a sum of squared correlation (SSCOR) for comparison.

For the horizontal comparative of the six surveyed recognition algorithms, this paper adopted the “Tsinghua JFPM-SSVEP” data set and compared the average recognition performance on such data set. The comparative contents including: recognition accuracy, ITR, correlated coefficient and R-square values under different time duration of the SSVEP stimulus presentation. Based on the optimal time duration of stimulus presentation, the author has also compared the efficiency of the six compared algorithms. To measure the influence of different parameters, the number of training trials, the number of electrodes and the usage of filter bank preprocessing were compared in the ablation study.

Based on the comparative results, this paper analyzed the advantages and disadvantages of the six compared SSVEP recognition algorithms by considering application scenes, real-time and computational complexity. Finally, the author gives the algorithms selection range for the recognition of real-world online SSVEP-BCI.

This paper describes the research underpinning the development and evaluation of a brain computer interface (BCI) system designed to be suitable for domestic use by people with acquired brain injury in order to facilitate control of their home environment. The purpose of the research is to develop a robust and user‐friendly BCI system which was customisable in terms of user ability, preferences and functionality. Specifically the human interface was designed to provide consistent visual metaphors in usage, while applications change, for example, from environmental control to entertainment and communications.

The research took a user centred design approach involving representative end‐users throughout the design and evaluation process. A qualitative study adopting user interviews alongside interactive workshops highlighted the issues that needed to be addressed in the development of a user interface for such a system. User validation then underpinned prototype development.

The findings of the research indicate that while there are still significant challenges in translating working BCI systems from the research laboratories to the homes of individuals with acquired brain injuries, participants are keen to be involved in the deign and development of such systems. In its current stage of development BCI is multi‐facetted and uses complex software, which poses a significant usability challenge. This work also found that the performance of the BCI paradigm chosen was considerably better for those users with no disability than for those with acquired brain injury. Further work is required to identify how and whether this performance gap can be addressed.

The research had significant challenges in terms of managing the complexity of the hardware and software set‐up and transferring the working systems to be tested by participants in their home. Furthermore, the authors believe that the development of assistive technologies for the disabled user requires a significant additional level of personalisation and intensive support to the level normally required for non‐disabled users. Coupled with the inherent complexity of BCI, this leads to technology that does not easily offer a solution to both disabled and non‐disabled users.

The research contributes additional findings relating to the usability of BCI systems. The value of the work is to highlight the practical issues involved in translating such systems to participants where the acquired brain injury can impact on the ability of the participant to use the BCI system.

This paper aims to apply a methodology that is capable to classify and localize mechanical, electrical and plumbing (MEP) elements to assist facility managers. Furthermore, it assists in decreasing the technical complexity and sophistication of different systems to the facility management (FM) team.

This research exploits artificial intelligence (AI) in FM operations through proposing a new system that uses a deep learning pre-trained model for transfer learning. The model can identify new MEP elements through image classification with a deep convolutional neural network using a support vector machine (SVM) technique under supervised learning. Also, an expert system is developed and integrated with an Android application to the proposed system to identify the required maintenance for the identified elements. FM team can reach the identified assets with bluetooth tracker devices to perform the required maintenance.

The proposed system aids facility managers in their tasks and decreases the maintenance costs of facilities by maintaining, upgrading, operating assets cost-effectively using the proposed system.

The paper considers three fire protection systems for proactive maintenance, where other structural or architectural systems can also significantly affect the level of service and cost expensive repairs and maintenance. Also, the proposed system relies on different platforms that required to be consolidated for facility technicians and managers end-users. Therefore, the authors will consider these limitations and expand the study as a case study in future work.

This paper assists in a proactive manner to decrease the lack of knowledge of the required maintenance to MEP elements that leads to a lower life cycle cost. These MEP elements have a big share in the operation and maintenance costs of building facilities.

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.

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.

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.

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 robotic wheelchair system was designed to assist disabled people with disabilities to walk.

An anticipated sharing control strategy based on topological map is proposed in this paper, which is used to assist robotic wheelchairs to realize interactive navigation. Then, a robotic wheelchair navigation control system based on the brain-computer interface and topological map was designed and implemented.

In the field of robotic wheelchairs, the problems of poor use, narrow application range and low humanization are still not improved.

In the system, the topological map construction is not restricted by the environment structure, which helps to expand the scope of application; the shared control system can predict the users’ intention and replace the users’ decision to realize human-machine interactive navigation, which has higher security, robustness and comfort.

The implementation of interaction design concepts to facilitate communication between users and shopping websites has gained increasing attention in recent years. Mouse hover is a vital interaction method for users to access shopping sites and significantly impacts their decision-making experience. A well-designed mouse hover function can effectively enhance the user's search performance and improve the user experience. The purpose of this study is to investigate whether the dynamic prompt designs at the hover position and the degree of feedback transparency may affect the user's task performance and personal feelings when operating the hover function on the shopping website.

The study employed two independent variables in the experiments: dynamic prompt and background transparency of hover feedback. A between-subject design of 2 (single flicker and continuous flicker) × 3 (transparency at 0%, 25%, and 50%) was adopted in the experiment. A total of 60 participants were invited to participate in the experiment using the purposive sampling method. Participants were asked to complete four operation tasks, and the time of each was recorded. They were then asked to complete the system usability scale (SUS) questionnaire and conduct subjective evaluations before they were briefly interviewed.

The generated results revealed that: (1) In the interaction with a shopping website, the degree of background transparency of hover feedback affected the participants' task performance. (2) The effect of a single flicker had significantly higher subjective evaluation results regarding the degree of rationality compared with a continuous flicker. (3) The participants' perceptions of the information clarity were affected when the background transparency of hover feedback was 50%. (4) The effect of a continuous flicker was better than that of a single flicker when the background transparency of hover feedback was 25%. The participants' attraction to a continuous flicker was significantly higher than that of a single flicker. Nonetheless, when the degree of background transparency was at 0% or 50%, the results were the opposite.

The findings generated from the research can be a reference for the development of hover operation in the user interface design for shopping websites.

This paper aims to extend the companion paper on “infant psychophysics”, which concentrated on the role of in-lab observers (watchers). Infants cannot report their own perceptions, so for five decades their detection thresholds for sensory stimuli were inferred from their stimulus-evoked behavior, judged by watchers. The inferred thresholds were revealed to inevitably be those of the watcher–infant duo, and, more broadly, the entire Laboratory. Such thresholds are unlikely to represent the finest stimuli that the infant can detect. What, then, do they represent?

Infants’ inferred stimulus-detection thresholds are hypothesized to be attentional thresholds, representing more-salient stimuli that overcome distraction.

Empirical psychometric functions, which show “detection” performance versus stimulus intensity, have shallower slopes for infants than for adults. This (and other evidence) substantiates the attentional hypothesis.

An observer can only infer the mechanisms underlying an infant’s perceptions, not know them; infants’ minds are “Black Boxes”. Nonetheless, infants’ physiological responses have been used for decades to infer stimulus-detection thresholds. But those inferences ultimately depend upon observer-chosen statistical criteria of normality. Again, stimulus-detection thresholds are probably overestimated.

Owing to exaggerated stimulus-detection thresholds, infants may be misdiagnosed as “hearing impaired”, then needlessly fitted with electronic implants.

Infants’ stimulus-detection thresholds are re-interpreted as attentional thresholds. Also, a cybernetics concept, the “Black Box”, is extended to infants, reinforcing the conclusions of the companion paper that the infant-as-research-subject cannot be conceptually separated from the attending laboratory staff. Indeed, infant and staff altogether constitute a new, reflexive whole, one that has proven too resilient for anybody’s good.

Models describe complex systems or events in simple terms. A better understanding is achieved by replacing intricate and complex systems with simpler and more familiar analogies. This paper deals with the complex visual system. The bibliographical survey given in this paper aims at showing the analogies adopted by authoritative workers in order to obtain a better understanding of “how and what we see”.