Search Results (42)

The rapid increase in global smartphone usage and the range of capabilities they offer have resulted in an overdependence on them, leading to the term nomophobia. Nomophobia refers to the psychological discomfort or anxiety experienced when an individual is unable to use or does not have access to their mobile phone, and it is a phenomenon that warrants research attention due to its psychological and social implications. The aim of the current study was to examine the relationship between nomophobia and the time spent on mobile usage, attachment in close romantic relationships, and loneliness. Participants included 300 adults from Cyprus who were recruited through convenience and snowball sampling methods. Data were gathered using an internet-based questionnaire that assessed participants’ time spent on mobile usage, their attachment styles in close relationships, and their level and type of loneliness. The results suggest that (a) anxiety dimension and time spent on mobile phone are significant predictors of nomophobia, (b) higher levels of nomophobia are associated with an insecure attachment style, (c) more severe levels of nomophobia are associated with higher levels of loneliness, and (d) increased time spent on mobile usage is linked to higher levels of nomophobia. The findings suggest that the widespread emergence of nomophobia raises important concerns, highlighting the need for the development of educational programs that promote balanced mobile usage and encourage direct social interaction. The significance of targeted interventions that address mobile phone regulation and attachment-related vulnerabilities is emphasized. Full article

Two-factor authentication (2FA) has become widely adopted as an efficient and secure way of validating someone’s identity online. Two-factor authentication is difficult in virtual reality (VR) because users are usually wearing a head-mounted display (HMD) which does not allow them to see their real-world surroundings. We present NRXR-ID, a technique to implement two-factor authentication while using extended reality systems and smartphones. The proposed method allows users to complete an authentication challenge using their smartphones without removing their HMD. We performed a user study in which we explored four types of challenges for users, including a novel checkers-style challenge. Users responded to these challenges under three different configurations, including a technique that uses a smartphone to support gaze-based selection without the use of a VR controller. A 4 × 3 within-subjects design allowed us to study all of the proposed variations. We collected performance metrics along with user experience questionnaires containing subjective impressions from thirty participants. Results suggest that the checkers-style visual matching challenge was the most preferred option, followed by the challenge involving entering a digital PIN submitted via the smartphone. Participants were fastest at solving the digital PIN challenge, with an average of 12.35 ± 5 s, followed by the Checkers challenge with 13.85 ± 5.29 s, then the CAPTCHA-style challenge with 14.36 ± 7.5 s, whereas the alphanumeric password took almost twice as long, averaging 32.71 ± 16.44 s. The checkers-style challenge performed consistently across all conditions with no significant differences (p = 0.185), making it robust to different implementation choices. Full article

This study investigated the link between identity processing styles, educational background, and psychological factors among engineering students in Kenyan TVET institutions in the Nyanza region. The research employs cluster analysis to identify student groups based on these variables. A total of 450 students from 15 public TVET institutions within the Nyanza region of Kenya comprised the study population. This pilot study included 110 students with ages ranging from 18 to 35 years. Data were collected by a self-administered online questionnaire. Based on cluster analysis, three groups of students were identified. The result revealed that 53.6% of the sample consisted of students with both diffuse-avoidant and normative identities; they were prone to academic procrastination and smartphone addiction and still possessed relatively higher levels of self-efficacy, life satisfaction, and academic performance/motivation. The second cluster included students with the highest level of informational identity (38.2%), good academic achievement, self-efficacy, optimism and life satisfaction, and motivation to learn. The third cluster consisted of students with low professional identity with poor academic performance and motivation, self-efficacy, and satisfaction with life (18.2%). The study’s findings can inform the development of targeted interventions to enhance student success and contribute to the effectiveness of vocational training programs. Full article

The aim of this current work is to identify three different gears of cross-country skiing utilizing embedded inertial measurement units and a suitable deep learning model. The cross-country style studied was the skating style during the uphill, which involved three different gears: symmetric gear pushing with poles on both sides (G3) and two asymmetric gears pushing with poles on the right side (G2R) or to the left side (G2L). To monitor the technique, inertial measurement units (IMUs) were affixed to the skis, recording acceleration and Euler angle data during the uphill tests performed by two experienced skiers using the gears under study. The initiation and termination points of the tests were controlled via Bluetooth by a smartphone using a custom application developed with Android Studio. Data were collected on the smartphone and stored on the SD memory cards included in each IMU. Convolutional neural networks combined with long short-term memory were utilized to classify and extract spatio-temporal features. The performance of the model in cross-user evaluations demonstrated an overall accuracy of 90%, and it achieved an accuracy of 98% in the cross-scene evaluations for individual users. These results indicate a promising performance of the developed system in distinguishing between different ski gears within skating styles, providing a valuable tool to enhance ski training and analysis. Full article

Some of the barriers preventing virtual reality (VR) from being widely adopted are the cost and unfamiliarity of VR systems. Here, we propose that in many cases, the specialized controllers shipped with most VR head-mounted displays can be replaced by a regular smartphone, cutting the cost of the system, and allowing users to interact in VR using a device they are already familiar with. To achieve this, we developed SmartVR Pointer, an approach that uses smartphones to replace the specialized controllers for two essential operations in VR: selection and navigation by teleporting. In SmartVR Pointer, a camera mounted on the head-mounted display (HMD) is tilted downwards so that it points to where the user will naturally be holding their phone in front of them. SmartVR Pointer supports three selection modalities: tracker based, gaze based, and combined/hybrid. In the tracker-based SmartVR Pointer selection, we use image-based tracking to track a QR code displayed on the phone screen and then map the phone’s position to a pointer shown within the field of view of the camera in the virtual environment. In the gaze-based selection modality, the user controls the pointer using their gaze and taps on the phone for selection. The combined technique is a hybrid between gaze-based interaction in VR and tracker-based Augmented Reality. It allows the user to control a VR pointer that looks and behaves like a mouse pointer by moving their smartphone to select objects within the virtual environment, and to interact with the selected objects using the smartphone’s touch screen. The touchscreen is used for selection and dragging. The SmartVR Pointer is simple and requires no calibration and no complex hardware assembly or disassembly. We demonstrate successful interactive applications of SmartVR Pointer in a VR environment with a demo where the user navigates in the virtual environment using teleportation points on the floor and then solves a Tetris-style key-and-lock challenge. Full article

Driving style and road traffic play pivotal roles in the development of smart cities, influencing traffic flow, safety, and environmental sustainability. This study presents an innovative approach for detecting road traffic conditions and driving styles using On-Board Diagnostics (OBD) data and smartphone sensors. This approach offers an inexpensive implementation of prediction, as it utilizes existing vehicle data without requiring additional setups. Two Artificial Neural Network (ANN) models were employed: the first utilizes a forward neural network architecture, while the second leverages bootstrapping or bagging neural networks to enhance detection accuracy for low-labeled classes. Support Vector Machine (SVM) is implemented to serve as a baseline for comparison. Experimental results demonstrate that ANNs exhibit significant improvements in detection accuracy compared to SVM. Moreover, the neural network with bagging model showcases enhanced recall values and a substantial improvement in accurately detecting instances belonging to low-labeled classes in both driving style road traffic. Full article

In this work, an intelligent hybrid model is proposed to identify hazardous or inattentive driving manoeuvres on roads, with the final goal being to increase and ensure travellers’ safety and comfort. The estimation is based on the effects that road geometry may have on vehicle accelerations, displacements and dynamics. The outputs of the intelligent systems proposed are how the type of driving can be characterized as normal, careless or distracted. The intelligent system consists of an LSTM (Long Short-Term Memory) neural network in a first step that distinguishes between normal and abnormal driving behaviour and then a second module that classifies abnormal forms of driving as aggressive or inattentive, with the latter implemented with another LSTM, a CNN (convolutional neural network) or the Hotelling transform. They are applied to some of the characteristics of vehicle dynamics to estimate the driving behaviour. Smartphone inertial sensors such as GPS, accelerometers and gyroscopes are used to measure these vehicle characteristics and to identify driving events in manoeuvres. Specifically, the critical acceleration due to the influence of the road geometry can be measured with inertial sensors, and then, this road acceleration with the lateral acceleration allows us to estimate the driver’s perceived acceleration. This perceived acceleration affects the driving style and, consequently, the estimation of the appropriate speed to travel on that road. There is use of both a traditional two-lane and a motorway route located in the Madrid region of Spain. Driving behaviour is determined by considering how changes in road geometry may affect one’s driving style and, consequently, the estimation of the proper speed. The results obtained with some of the proposed configurations of the intelligent hybrid system reach an accuracy of 97.21% in detecting dangerous driving or driving with a certain risk. This could allow generating real-time alerts for potentially dangerous or inattentive manoeuvres, leading to safer and more appropriate driving. Full article

The transportation sector remains a major contributor to greenhouse gas emissions. The understanding of energy-efficient driving behaviors and utilization of energy-efficient driving strategies are essential to reduce vehicles’ fuel consumption. However, there is no comprehensive investigation into energy-efficient driving behaviors and strategies. Furthermore, many state-of-the-art AI models have been applied for the analysis of eco-friendly driving styles, but no overview is available. To fill the gap, this paper conducts a thorough literature review on ecological driving behaviors and styles, and analyzes the driving factors influencing energy consumption and state-of-the-art methodologies. With a thorough scoping review process, thirty-seven articles with full text were assessed, and the methodological and related data are compared. The results show that the factors that impact driving behaviors can be summarized into eleven features including speed, acceleration, deceleration, pedal, steering, gear, engine, distance, weather, traffic signal, and road parameters. This paper finds that supervised/unsupervised learning algorithms and reinforcement learning frameworks have been popularly used to model the vehicle’s energy consumption with multi-dimensional data. Furthermore, the literature shows that the driving data are collected from either simulators or real-world experiments, and the real-world data are mainly stored and transmitted by meters, controller area networks, onboard data services, smartphones, and additional sensors installed in the vehicle. Based on driving behavior factors, driver characteristics, and safety rules, this paper recommends nine energy-efficient driving styles including four guidelines for the drivers’ selection and adjustment of the vehicle parameters, three recommendations for the energy-efficient driving styles in different driving scenarios, and two subjective suggestions for different types of drivers and employers. Full article

Every day, we make thousands of finger movements on the touchscreen of our smartphones. The same movements might be directed at various distal goals. We can type “What is the weather in Rome?” in Google to acquire information from a weather site, or we may type it on WhatsApp to decide whether to visit Rome with a friend. In this study, we show that by watching an agent’s typing hands, an observer can infer whether the agent is typing on the smartphone to obtain information or to share it with others. The probability of answering correctly varies with age and typing style. According to embodied cognition, we propose that the recognition process relies on detecting subtle differences in the agent’s movement, a skill that grows with sensorimotor competence. We expect that this preliminary work will serve as a starting point for further research on sensorimotor representations of digital actions. Full article

Background: Problematic smartphone use has increasingly become the focus of attention in recent years. Although it has been noted that parental psychological control is significantly correlated with teenagers’ social anxiety and problematic smartphone use, little is known about how these factors may interact with college students. Therefore, the purpose of this study is to investigate whether social anxiety mediates the association between parental psychological control and problematic smartphone use. Methods: a total of 534 Chinese college students aged 17–25 years (male 59.0%, female 41.0%) participated in the study (M = 20.40, SD = 1.72). The Parental Psychological Control questionnaire, the Social Phobia Inventory, and the Mobile Phone Addiction Tendency Scale were used to evaluate parental psychological control, social anxiety, and problematic smartphone use, respectively. Data were analyzed using the Pearson correlation analysis, regression analysis, and mediation analysis. Results: the results showed that (1) social anxiety was positively correlated with problematic smartphone use among college students, (2) parental psychological control has a significant correlation with college students’ social anxiety, (3) college students’ social anxiety was positively related with problematic smartphone use, and (4) social anxiety plays a mediation role in the association between parental psychological control and problematic smartphone use. Conclusions: in conclusion, social anxiety plays a mediating role in the relationship between parental psychological control and problematic smartphone use, and reducing parental psychological control is an effective intervention means to directly or indirectly reduce college students’ problematic smartphone use. In addition, attention should be paid to parenting styles, and measures should be taken to increase social interaction among college students so as to reduce their problematic smartphone use. Full article

In recent years, there has been a surge of Metaverse applications and tools striving to capture the attention of both the general public and businesses, with a particularly strong potential within the tourism sector. However, there has been significant criticism towards major corporations for marketing a concept of the Metaverse that fails to align with reality. On the other hand, smaller entities such as Spatial-io, which is an innovative metaverse platform, are introducing a different style of the Metaverse, one that is highly accessible from contemporary devices like smartphones, tablets, VR headsets, and traditional PCs via WebXR platforms. This article delves into and scrutinizes various methodologies of a tourism-oriented Metaverse, considering its prospective utility as a vehicle to attract more visitors. A virtual tourist information center was established on the Spatial-io Metaverse platform to promote Valencia, Spain. This research scrutinizes the navigation, accessibility, and usability of the service from a conventional PC browser, contrasting it with the experience offered by the Meta Quest 2 virtual reality headset. The study’s quantitative and qualitative data analysis indicates that these innovative services are highly regarded, particularly when a real person (not a bot) provides information, fostering trust and offering details about various tourist attractions within the promoted city. The comparison of user inquiries’ time and depth aligns with the immersion level, demonstrating more positive feedback when the service is accessed through the VR system rather than a standard PC browser. Full article

In recent times, there has been a noticeable increase in the application of human biometrics for user authentication in various domains, such as online banking. However, the use of biometric systems poses security risks and the potential for misuse, primarily due to the storage of original templates in databases. To tackle this issue, the concept of cancelable biometrics has emerged as a reliable method utilizing one-way encryption. Several algorithms have been developed to implement cancelable biometrics, incorporating visual representations of single or multiple biometrics. This research proposes a cancelable biometric system that utilizes deep learning techniques to generate two encrypted modalities, namely text and image, using facial and fingerprint biometrics acquired from a smartphone. The system consists of two main stages: a visual encoder and a text encoder. The visual encoder converts the fingerprint style into a facial representation, creating a cancelable template to ensure the potential for cancelation. The resulting visual template is then processed by the text encoder, which employs hashing techniques to generate a corresponding text template. User authentication is automatically verified by utilizing the generated templates through Siamese networks. Full article

Self-harm and other mental health difficulties are very common amongst university students, but students face numerous barriers in accessing professional support. Support offered via a smartphone app may help to overcome some of the barriers they face, while providing support that is acceptable and helpful. However, there is limited research on supportive apps for students who self-harm. This study aimed to evaluate a self-help app (BlueIce) for helping students manage their self-harm, mental wellbeing and coping ability. This was a pre-post study in which 80 participants completed baseline measures online and were sent a link to download BlueIce. Of these, 27 completed follow-up questionnaires six weeks later assessing anxiety, depression, self-harm, and coping self-efficacy/styles. At follow-up, participants also completed a questionnaire evaluating BlueIce. Self-harm urges and symptoms of anxiety and depression significantly decreased, and coping self-efficacy significantly increased. Around two thirds (64%) said that BlueIce had stopped them from harming themselves an average of 24 times. Feedback showed that BlueIce helped provide a distraction in difficult times and helped them to manage their emotions in a more adaptive way. Following the trial period, participants’ wellbeing had significantly improved, suggesting that BlueIce may be helpful for university students in managing their self-harm urges and general mental health. Full article

In today’s digital age, where smartphones are ubiquitous among the younger generation, they can add to the cognitive load on the brain, even when not in use. This can affect students’ learning outcomes and creativity, leading to negative emotions or creativity blocks during the learning process. Thus, this study investigates the relationship between differences in students’ cognitive styles and their learning motivation, learning outcomes, creativity, and learning satisfaction. The primary objective is to use the STEAM-6E instructional model in virtual reality (VR) courses to understand how students with different cognitive styles can be stimulated to unleash their diverse and vibrant creativity based on their learning preferences during hands-on experiences. The study also aims to explore whether there are disparities in their learning motivation and learning outcomes, and whether there are differences in their overall learning satisfaction. The findings of the study indicate that for the two cognitive styles of holistic and sequential, the subjects showed significant differences in their learning motivation regarding intrinsic goals, extrinsic goals, task value, control beliefs, self-efficacy, and test anxiety. Significant differences were also observed in their learning preferences, learning outcomes, and creative performance. However, the two groups had no significant differences in the effectiveness, efficiency, and overall satisfaction of the learning activities. Full article

This paper presents an integrated monitoring system for the driver and the vehicle in a single case of study easy to configure and replicate. On-board vehicle sensors and remote sensors are combined to model algorithms for estimating polluting emissions, fuel consumption, driving style and driver’s health. The main contribution of this paper is the analysis of interactions among the above monitored features highlighting the influence of the driver in the vehicle performance and vice versa. This analysis was carried out experimentally using one vehicle with different drivers and routes and implemented on a mobile application. Compared to commercial driver and vehicle monitoring systems, this approach is not customized, uses classical sensor measurements, and is based on simple algorithms that have been already proven but not in an interactive environment with other algorithms. In the procedure design of this global vehicle and driver monitoring system, a principal component analysis was carried out to reduce the variables used in the training/testing algorithms with objective to decrease the transfer data via Bluetooth between the used devices: a biometric wristband, a smartphone and the vehicle’s central computer. Experimental results show that the proposed vehicle and driver monitoring system predicts correctly the fuel consumption index in 84%, the polluting emissions 89%, and the driving style 89%. Indeed, interesting correlation results between the driver’s heart condition and vehicular traffic have been found in this analysis. Full article