Augmented Human Research最新文献

In a controlled network environment, such as the smart indoor manufacturing environment, the device identification and detection of components is challenging without prior knowledge of the design and implementation process. Thus, the concept of device identification for diagnosis and equipment maintenance by means of markerless augmented reality (AR) merits investigation. AR, when coupled with machine vision, caters for obtaining real-time device information regarding the position and features of the robotic elements within indoor manufacturing plants. Thus, this article proposes an efficient machine vision model to detect and identify devices based within a manufacturing plant, with the aid of AR for extending the device operational details. This offers an alternative solution in the absence of user built-in maps for the calculation of device positions based on uncertainties of the exact locations. To achieve this, a two-part validation is conducted involving (1) device recognition based on position and (2) Data integration to A Supervisory Control and Data Acquisition (SCADA) model developed in National Instruments Labview. The findings demonstrate that the AR application can detect devices within the manufacturing plant without the need for alteration. The results also indicate that the application can be integrated into a SCADA model without the need to alter the application, provided that the array index is the same. Only when the array index differs are alterations necessary for utilising the AR application.

In the game of cricket, there are various kinds of dismissals that can be caused due to multiple reasons. Few of them include the bowler’s brilliance and the batsman’s mistake. Run-out being one kind of dismissal, that completely changes the fortune and momentum of teams. Most of the time, it becomes difficult for the on-field umpire to give a judgement on run-out with naked eyes. So, the decisions are transferred to the third umpire, who gives the final decision based on a time-consuming technique. Therefore, we are proposing an approach, in which dismissal prediction is made using object detection and support vector machine. As the dismissal prediction is made based on images from different angles from various cameras, we were successful in achieving an accuracy rate of 87%. Additionally, since it works on an automated process, it is much more time-efficient than the traditional system. Thus by using this approach, errors are minimized and machine learning capabilities are provided to decision making in the game of cricket.

Food waste is a significant challenge, and our societal behaviours play a role in the amount of food items discarded. Thus, an effective method to inform consumers about high wastage patterns may help reduce the amount thrown away. This research investigates how Augmented Reality can be harnessed to enlighten consumers and work towards addressing high food waste patterns. Yet research on this topic is still very much in its infancy. To pursue this solution, food behaviour data are employed to provide an insight into how much is wasted from 9 catering industry locations in the Netherlands. An Augmented Reality application is developed, where models of food are projected onto real-world environments to provide scale on waste over a 7-day period. A quantitative evaluation of higher-education attendees demonstrated the approach has potential to incentivise reduction in waste.

Our travel planner mobile application was designed to fit the traveling post to COVID-19 outbreak following the lean product process principle. We intend to develop a travel application that brings back the joy of traveling. The objective of this study is to design a travel planner application that satisfies the user’s needs. In the first stage of the lean product process, we first determine our target customer by interviewing eight millennials. Once the persona was clarified, we explored the problem space, identified underserved customer needs, and prioritized those needs. The features with high importance but were underserved are COVID-19 guideline information, place recommendation, route optimization, and price comparison, which we believe could offer excellent opportunities to create customer value. The value proposition and feature set were also identified before creating the first MVP prototype. The detailed navigation flow and interactive MVP prototype were created and then tested with users. As a result, in the last iteration of usability testing, the value rating was increased from 6 to 9, and the ease of use rating was increased from 6 to 8 compared to the first rounds.

Theme entertainment businesses rely very much on providing their visitors with the most memorable experiences. To do so, top operators have put a considerable amount of effort and investment in their facilities and activities in their parks. However, after a customer interview, several pain points are still found such as long toilet queues, finding available parking lots, being unable to find the parked car, and no real-time thrill ride information/status. Similar with other domains in the tourism market, most of these problems are easy to solve with the help of having a smartphone application that delivers such information right to the customer’s hands. This study outlines the development process of a theme park application based on a design thinking approach. Our MVP prototypes have obtained good feedback from potential users in both qualitative and quantitative usability tests.

At the end of December 2019, the pandemic COVID-19 disease emerged in Wuhan city of China. It spread rapidly and affects other parts of China. To control the risk of further spread of disease the authorities in Wuhan locked down the city on 23 January 2020. The COVID-19 cases were detected in several other countries within a few weeks, and it became a global threat. On 6th June 2020, 115,942 active COVID-19 cases exist in India. To break the chain of transmission of infectious disease, the educational institutions are closed. Due to the COVID-19 pandemic, all schools/colleges/universities have postponed or cancelled all campus events such as examinations, workshops, conferences, admissions and other activities. The main objective of this study is to know the impact of COVID-19 on education, to study the perception of students towards online education during COVID-19 crisis and to study the student perception towards COVID-19 disease. The data are collected by structured questionnaire from 100 students in Tirupati city. The study includes both primary and secondary data. The convenient sampling method is used to collect data. Results are summarized using SPSS software and conclusion drawn that most of the respondents are satisfied towards online education and most of the students want to continue the online education in future also. The main problem facing by the students is network connectivity. In Tirupati, 45% of the colleges are using Canvas Instructure for online classes. To build young minds in the present crisis, an effective and well-rounded educational practice is needed. It will help students to improve their skills that will drive their productivity, employability, health and well-being in the decades to come and ensure the overall progress of India.

Over recent decades smart glasses have gained increased attention in both the research arena and recently also in the consumer market, even though there is not yet a clear definition of what exactly smart glasses entail and underexposed perspectives are not represented. This study used a rapid review to assess the current understanding of smart glasses with the aim of defining them. Searches were performed across six databases, followed-up by a content-based evaluation of title and abstract. A total set of 14 relevant publications was identified to help arrive at a definition and characteristics of smart glasses. As a result, it was observed in both the research literature and in the public domain that many different names are used for smart glasses, and that in some cases there is unclarity about what constitute smart glasses. Therefore, an adapted definition of smart glasses is developed based on the existing original rationale of ubiquitous computing and taking the current state-of-the-art knowledge into account. This article provides an overview of and suggestion for defining smart glasses from a social sciences’ perspective to better inform researchers, developers, designers and companies who are involved in the design, development, research of smart glasses.

Blind people can benefit greatly from a system capable of localising and reading comprehension text embedded in natural scenes and providing useful information that boosts their self-esteem and autonomy in everyday situations. Regardless of the fact that existing optical character recognition programmes seem to be quick and effective, the majority of them are not able to correctly recognise text embedded in usual panorama images. The methodology described in this paper is to localise textual image regions and pre-process them using the naïve Bayesian algorithm. A weighted reading technique is used to generate the correct text data from the complicated image regions. Usually, images hold some disturbance as a result of the fact that filtration is proposed during the early pre-processing step. To restore the image's quality, the input image is processed employing gradient and contrast image methods. Following that, the contrast of the source images would be enhanced using an adaptive image map. The stroke width transform, Gabor’s transform, and weighted naïve Bayesian classifier methodologies have been used in complicated degraded images to segment, feature extraction, and detect textual and non-textual elements. Finally, to identify categorised textual data, the confluence of deep neural networks and particle swarm optimisation is being used. The text in the image is transformed into an acoustic output after identification. The dataset IIIT5K is used for the development portion, and the performance of the suggested come up is evaluated using parameters such as accuracy, recall, precision, and F1-score.

Tourism is the main source of economic growth and a creator of employment of various kinds. The Rajasthan state got the fifth rank in the top 10 tourist destinations among domestic tourism ranks. The main inflection of this paper is to investigate the influence of tourist satisfaction of international tourists on future behavioral intentions with special reference to the desert triangle of Rajasthan. The holiday satisfaction model was used to construct the survey method applied in this study. According to the findings, tourist holiday satisfaction is particularly high for attraction qualities in numerous tourist sites in Rajasthan's desert triangle. The HOLSAT model was used to calculate the average mean of experience and expectation in this study, and the results show that the attributes described as attractions, accessibility and activities components are those that foreign visitors value the most. International tourists, on the other hand, give the lowest scores to qualities classed under amenities and accommodation components, indicating that international tourism has a long way to go.Thus, the tourism sector will need to guide human resources to curate and deliver these experiences.

The rapid outbreak of COVID-19 is a global health problem that has a significant effect on the educational systems. Therefore, students shifted to distance learning through the digital platform. Since COVID-19 has consequences on mental health, the present study examined the perceived stress level in school students in distance learning during the COVID-19 period. A cross-sectional study of a sample consisting of 385 school students evaluated the Perceived Stress Scale (PSS) and their concerns and emotions during the COVID-19 pandemic. The results showed that the mean value of the total PSS scores was 19.50 ± 9.28. About 48.6% of students had moderate stress, 28.8% had low stress, and 22.6% had high stress. Significant associations were found between the level of stress and demographic variables: age, gender, educational level, and place of residence. Female students were significantly higher than the male students in the mean score of perceived stress level (20.14 versus 18.13; p-value = 0.018). Females were more likely (53.4%) to have moderate stress than males (38.2%) (p = 0.019). The students aged 15–18 years were significantly higher than those aged 11–14 years in the mean score of perceived stress level (22.64 versus 17.24, respectively; p-value < 0.001). The students aged 15–18 years were also more likely (n = 536, 96.4%) to had high levels of stress than the younger students (p < 0.001). The results showed that secondary school students were significantly higher than primary school students in the mean score of perceived stress level (23.75 versus 16.76, respectively; p < 0.001). Secondary students were also more likely (43.0%) to have high stress than primary students (9.4%) (p < 0.001). Students who live in the Rafah governorate had a significantly lowest mean score of perceived stress level when compared to other counterparts (p-value = 0.005). Gender (β = − 0.282, P < 0.001), age (β = − 0.379, P = 0.004), place of residence (β = − 0.096, P = 0.034), and educational level (β = 0.845, P < 0.001) were predictors of perceived stress. The results revealed moderate to high levels of stress in school students in the Gaza Strip during schools closure due to the COVID-19 pandemic. The prevalence of psychological distress attributed to suddenly shifted from traditional learning to distance learning. The incorporation of stress management programs and online counselling will minimize stress and enhance the mental health of school students during distance learning.