Visual Vigilance Research Articles

Design and Implementation of a GSM-Based Home Protection System Against Gas Cylinder Explosion

The aim of this work is to design and implement a GSM-based smart home protection system against the possible life-threatening firing and damage of properties due to gas cylinder explosions. These hazardous explosions are increasingly happened in Bangladesh in the recent years and hence, become serious concerns for the safety of individuals residing in a house. The system designed and implemented in this work can identify unnatural gas leakage by using gas sensors and also, sense unusual temperature increases due to firing inside a house or a restaurant by using temperature sensors. The proposed system ensures multi-level security. The audio and visual alerts are ensured by the use of fire alarms and LEDs respectively. Using GSM module, the system also makes emergency calls and sends SMS to the owner and the nearby fire station to take prompt actions. One servo motor stops the gas valve to cease the gas leakage and another one starts the water valve to flow water to reduce the temperature. Additionally, an exhaust fan is used to reduce the room temperature and let the gas flow out. Therefore, the proposed system is a potential solution to reduce life risks and property damage due to gas cylinder explosion.

Real-Time Forward Head Posture Detection and Correction System Utilizing an Inertial Measurement Unit Sensor

Forward head posture (FHP) has become a prevailing health issue in modern society as people spend more time on computers and smartphones. FHP is a posture where the head is forward and the anterior and posterior curvatures of the lower cervical and upper thoracic spines are both, respectively, exaggerated. FHP is often associated with neck pain, bad static balance, and hunched shoulders or back. To prevent this, consciously maintaining good posture is important. Therefore, in this study, we propose a system that gives users real-time, accurate information about their neck posture, and it also encourages them to maintain a good posture. This inexpensive system utilizes a single inertial measurement unit sensor and a Raspberry Pi system to detect the changes in state that can progress to an FHP. It retrieves data from the sensor attached to the user’s cervical spine to indicate their real-time posture. In a real-world office environment experiment with ten male participants, the system accurately detected the transition to the FHP state for more than 10 s, with a delay of less than 0.5 s, and it also provided personalized feedback to encourage them to maintain good posture. All ten participants recognized that their average craniovertebral angle had to be increased after receiving visual alerts regarding their poor postures in real time. The results indicate that the system has potential for widespread applications.

Smart LPG Regulator

The Smart LPG Regulator is designed to oversee the regulation of pressurized steel gas cylinders in residential households. The primary goal of this project is to prevent the potential risks associated with LPG gas leaks, including the loss of life, injuries, and health concerns. The system is equipped to identify gas leaks and issue auditory and visual alerts through a buzzer, a display, and a servo motor, which promptly closes the gas cylinder regulator to terminate the gas supply. The LPG Gas Detector (MQ6) is a suitable sensor with high sensitivity to Liquid Petroleum Gas (LPG), including propane, proficient, isobutene, and natural gas. It is also capable of detecting easily flammable gases, mainly methane. Data collected by the MQ6 sensor is processed by the Atmega 328P microcontroller and presented through the output components. The LCD screen will display 'GAS LEAK,' the buzzer will sound an alarm, and the servo motor will continue to close the regulator valve on the gas cylinder. The MQ6 sensor can identify gas leaks from the gas cylinder, gas pipes, or kitchen stove burners. One significant advantage of this system is its ability to shut off the gas cylinder supply, indirectly preventing accidents and injuries. The system's design is tailored to accommodate the standard size of gas cylinders used in residential homes, with domestic consumers as the target user group.

Speech, Nonspeech Audio, and Visual Interruptions of a Tracking Task: A Replication and Extension of Nees and Sampsell (2021)

Interruptions from technology—such as alerts from mobile communication devices—are a pervasive aspect of modern life. Interruptions can be detrimental to performance of the ongoing, interrupted task. Designers often can choose whether interruptions are delivered as visual or auditory alerts. Contradictory theories have emerged regarding whether auditory or visual alerts are more harmful to performance of ongoing visual tasks. Multiple Resources Theory predicts better overall performance with auditory alerts, but Auditory Preemption Theory predicts better overall performance with visual alerts. Nees and Sampsell previously found that multitasking was superior with nonspeech auditory alerts as compared to visual alerts. In the current experiment, their methods were replicated and extended to include a speech auditory alerts condition. Performance of the ongoing tracking task was worse with interruption from visual alerts, and perceived workload also was highest in this condition. Reaction time to alerts was fastest with visual alerts. There also was converging evidence to suggest that performance with speech alerts was superior to performance with nonspeech tonal alerts. The current experiment replicated the results of Nees and Sampsell and extended their findings to speech alert sounds. Like in their study, the pattern of findings here support Multiple Resources Theory over Auditory Preemption Theory.

Auditory vigilance task performance and cerebral hemodynamics: effects of spatial uncertainty.

The vigilance decrement, a temporal decline in detection performance, has been observed across multiple sensory modalities. Spatial uncertainty about the location of task-relevant stimuli has been demonstrated to increase the demands of vigilance and increase the severity of the vigilance decrement when attending to visual displays. The current study investigated whether spatial uncertainty also increases the severity of the vigilance decrement and task demands when an auditory display is used. Individuals monitored an auditory display to detect critical signals that were shorter in duration than non-target stimuli. These auditory stimuli were presented in either a consistent, predictable pattern that alternated sound presentation from left to right (spatial certainty) or an inconsistent, unpredictable pattern that randomly presented sounds from the left or right (spatial uncertainty). Cerebral blood flow velocity (CBFV) was measured to assess the neurophysiological demands of the task. A decline in performance and CBFV was observed in both the spatially certain and spatially uncertain conditions, suggesting that spatial auditory vigilance tasks are demanding and can result in a vigilance decrement. Spatial uncertainty resulted in a more severe vigilance decrement in correct detections compared to spatial certainty. Reduced right-hemispheric CBFV was also observed during spatial uncertainty compared to spatial certainty. Together, these results suggest that auditory spatial uncertainty hindered performance and required greater attentional demands compared to spatial certainty. These results concur with previous research showing the negative impact of spatial uncertainty in visual vigilance tasks, but the current results contrast recent research showing no effect of spatial uncertainty on tactile vigilance.

VespAI: a deep learning-based system for the detection of invasive hornets

The invasive hornet Vespa velutina nigrithorax is a rapidly proliferating threat to pollinators in Europe and East Asia. To effectively limit its spread, colonies must be detected and destroyed early in the invasion curve, however the current reliance upon visual alerts by the public yields low accuracy. Advances in deep learning offer a potential solution to this, but the application of such technology remains challenging. Here we present VespAI, an automated system for the rapid detection of V. velutina. We leverage a hardware-assisted AI approach, combining a standardised monitoring station with deep YOLOv5s architecture and a ResNet backbone, trained on a bespoke end-to-end pipeline. This enables the system to detect hornets in real-time—achieving a mean precision-recall score of ≥0.99—and send associated image alerts via a compact remote processor. We demonstrate the successful operation of a prototype system in the field, and confirm its suitability for large-scale deployment in future use cases. As such, VespAI has the potential to transform the way that invasive hornets are managed, providing a robust early warning system to prevent ingressions into new regions.

Effects of Browsing Conditions and Visual Alert Design on Human Susceptibility to Deepfakes

The increasing reach of deepfakes raises practical questions about people’s ability to detect false videos online. How vulnerable are people to deepfake videos? What technologies can help improve detection? Previous experiments that measure human deepfake detection historically omit a number of conditions that can exist in typical browsing conditions. Here, we operationalized four such conditions (low prevalence, brief presentation, low video quality, and divided attention), and found in a series of online experiments that all conditions lowered detection relative to baseline, suggesting that the current literature underestimates people’s susceptibility to deepfakes. Next, we examined how AI assistance could be integrated into the human decision process. We found that a model that exposes deepfakes by amplifying artifacts increases detection rates, and also leads to higher rates of incorporating AI feedback and higher final confidence than text-based prompts. Overall, this suggests that visual indicators that cause distortions on fake videos may be effective at mitigating the impact of falsified video.

Driver Drowsiness Detection System

The Driver Drowsiness Monitoring System (DDMS) stands as a crucial advancement in automotive safety, specifically designed to mitigate accidents stemming from drowsy driving. Employing a sophisticated blend of sensors and advanced algorithms, it continuously monitors the driver's state, identifying signs of drowsiness in real-time. Key elements encompass infrared cameras, facial recognition technology, steering angle sensors, and biometric sensors such as heart rate monitors, collectively offering a comprehensive insight into the driver's behavior and physiological condition. The DDMS software meticulously analyzes data from these sensors, evaluating parameters like eye movement, blink frequency, facial expressions, and steering patterns. Upon detecting signs of drowsiness, the system promptly issues audible and visual alerts, thereby averting potential accidents caused by driver fatigue. Its adaptability to diverse vehicles and driving conditions establishes it as an indispensable tool for bolstering road safety, effectively curbing drowsy driving-related incidents.

Designing Prototype of Volume Detector for Medical Oxygen Cylinder Using NodeMCU ESP8266

The COVID-19 pandemic posed significant challenges to the healthcare sector, particularly due to the imbalance between patient numbers and available medical staff, resulting in difficulties during emergencies. One such challenge was the prompt replacement of oxygen cylinders. Leveraging Internet of Things (IoT) technology, we developed a prototype oxygen cylinder volume detector aimed at providing early notifications to healthcare professionals. This prototype incorporates the NodeMCU ESP8266, equipped with an Infrared (IR) Obstacle Sensor. The sensor communicates with a relay to activate an MP3 module, delivering audible alerts. Furthermore, the NodeMCU ESP8266 system integrates CTBot to send text notifications through the Telegram application to the nearest healthcare personnel. Visual alerts are also provided through red LED lights attached to the cylinders, indicating which oxygen cylinder is approaching depletion. Comprehensive testing validated the proper functionality of all system components. This innovative oxygen cylinder detector prototype is designed to streamline healthcare worker and nurse workflows by offering quicker access to accurate information regarding the status of medical oxygen cylinders. Not only does this enhance patient care efficiency, but it also ensuring the availability of oxygen is crucial for critical patients.

Effects of Spatial Uncertainty on Tactile Vigilance

We examined effects of task demand on tactile vigilance by manipulating spatial uncertainty, which has been shown to affect visual vigilance. While holding a tactor in each hand, participants received a long or short (critical) vibration, and pressed a foot pedal when the critical stimulus occurred. In the “certain” condition, the spatial location of the stimulus alternated predictably across hands. In the “uncertain” condition, the stimulus was randomly assigned to either hand. We measured response time; proportion hits and false alarms; sensitivity and response bias; and stress and workload. Vigilance decrement was observed; sensitivity decreased and response time increased. Spatial uncertainty did not affect the occurrence or magnitude of the decrement. Results suggest that effects of cognitive demands are more similar between tactile and auditory modalities compared to the visual modality. Interface designers should be aware that spatial uncertainty likely has more detrimental effects on visual vigilance, compared to tactile vigilance.

Improving hazard perception for drivers based on driving styles with visual alerts

This paper aims to investigate the influence of driving styles on dangers by testing drivers' hazard perception with driving style variability in different scenarios and applying visual alerts to improve driver hazard perception. To achieve such goals, virtual reality systems are employed for simulations. In the experiment, we tested drivers' hazard perception with four driving styles (dangerous, angry, anxious, and cautious) in different hazard conditions and improved hazard perception through visual alert training. Participants' driving behavior and hazard perception were recorded simultaneously. The results showed that (1) Dangerous drivers have the weakest perception of danger, cautious drivers are the strongest, and anxious and angry drivers are similar. (2) Visual alerts can effectively improve drivers' hazard perception and help them mitigate hazards. (3). Visual alerts are more effective for dangerous drivers in improving hazard perception and mitigating hazards. The study offers important implications for road safety with visual alert training by improving hazard perception based on driving style.

How Brazilian oral health care workers face COVID-19

Aim: To investigate surveillance, biosafety, and education strategies of Brazilian oral health care workers (OHCWs) during the first wave of the COVID-19 outbreak. Methods: This was a cross-sectional study covering OHCWs from a single multicenter research centre. A self-administered and validated online questionnaire was used for data collection, including the following variables: sociodemographic, medical history, biosafety, professional experience, surveillance, and education. Results: The sample consisted of 644 OHCWs (82.5% dentists, 13.2% dental assistants and 4.3% technicians), most without comorbidities (84.8%), from the public (51.7%) and private (48.3%) health systems, in 140 cities of a southern state. The most prominent measures of surveillance were waiting room distancing and visual alerts, symptom assessment, and availability of guidelines on COVID-19. Regarding biosafety measures, the lowest adherence was related to intraoral radiographs (2.7±1.4; 95%CI: 2.6–2.9), use of dental dams (2.1±1.4; 95%CI: 2.0–2.2), and availability of high-power suction systems (2.5±1.7; 95%CI: 2.3–2.6). Among OHCWs, 52.6% received guidance on measures to take during dental care in the workplace. Continuing education was mainly through documents from non-governmental health authorities (77.4%). Conclusion: Surveillance and biosafety measures were adopted, but activities that reduce the spread of aerosols had less adherence. These findings underscore the importance of considering dental practices, and surveillance and education strategies to formulate policies and relevant support to address health system challenges during the COVID-19 pandemic. A coordinated action of permanent education by policymakers is necessary.

Application of artificial intelligence to build a security control software system in local military units

This paper introduces the application of artificial intelligence to build a security control software system in local military units. This software system uses state-of-the-art convolutional neural networks (CNN SOTA) for facial recognition by testing two of the best facial recognition models currently available: the FaceNet model and the VGGFace model. Through testing on the proposed hardware, the FaceNet model meets the accuracy and speed requirements for practical application. The software includes multiple identity management categories to ensure information security and monitor the access of soldiers and other individuals. Additionally, the software features access management functions for protected areas, allowing for audio and visual alerts to ensure safety and security in those areas. The software also enables users to set up connections with other devices for efficient data collection and processing. Simultaneously, it supports synchronized data connection to help users save time and effort in managing information. Moreover, the software includes user-friendly interfaces and customizable settings, ensuring ease of use and adaptability to the specific needs of each military unit. this software system provides a comprehensive and effective solution for ensuring security and monitoring access in local military units. By leveraging artificial intelligence, the system can adapt and improve over time, offering enhanced performance and capabilities to meet the evolving security needs of military organizations. The innovative approach presented in this paper has the potential to significantly improve the overall security and efficiency of local military units, contributing to the safety and well-being of both military personnel and the communities they serve.

The effects of self-selected background music and task difficulty on task engagement and performance in a visual vigilance task.

Listening to self-selected background music has been shown to be associated with increased task focus and decreased mind wandering during a sustained attention task (Kiss and Linnell, Psychological Research Psychologische Forschung 85:2313-2325, 2021). It is unclear, however, how this relation may depend on the potentially critical factor of task difficulty. To address this knowledge gap, we explored how listening to self-selected music, compared to silence, affects subjectively experienced task engagement (i.e., task focus, mind wandering, and external distraction/bodily sensation states) and task performance during either an easy or a hard vigilance task. We also examined how these effects vary with time-on-task. Our results replicated prior work demonstrating that background music enhanced task focus and decreased mind wandering, compared to silence. There was also lower reaction time variability in the background music condition relative to the silence condition. Notably, these findings held regardless of task difficulty. Interestingly, when examined over time-on-task, the presence of music led to smaller task focus declines and mind wandering increases, compared to silence. Thus, listening to self-selected music appears to confer a protective effect on task engagement, especially over time-on-task.

Visual alertness and brain diffusion tensor imaging at term age predict neurocognitive development at preschool age in extremely preterm-born children.

Cognitive development is characterized by the structural and functional maturation of the brain. Diffusion-weighted magnetic resonance imaging (dMRI) provides methods of investigating the brain structure and connectivity and their correlations with the neurocognitive outcome. Our aim was to examine the relationship between early visual abilities, brain white matter structures, and the later neurocognitive outcome. This study included 20 infants who were born before 28 gestational weeks and followed until the age of 6.5 years. At term age, visual alertness was evaluated and dMRI was used to investigate the brain white matter structure using fractional anisotropy (FA) in tract-based spatial statistics analysis. The JHU DTI white matter atlas was used to locate the findings. The neuropsychological assessment was used to assess neurocognitive performance at 6.5 years. Optimal visual alertness at term age was significantly associated with better visuospatial processing (p<.05), sensorimotor functioning (p<.05), and social perception (p<.05) at 6.5 years of age. Optimal visual alertness related to higher FA values, and further, the FA values positively correlated with the neurocognitive outcome. The tract-based spatial differences in FA values were detected between children with optimal and nonoptimal visual alertness according to performance at 6.5 years. We provide neurobiological evidence for the global and tract-based spatial differences in the white matter maturation between extremely preterm children with optimal and nonoptimal visual alertness at term age and a link between white matter maturation, visual alertness and the neurocognitive outcome at 6.5 years proposing that early visual function is a building block for the later neurocognitive development.

HIDRATE-PRO: A prospective pilot study evaluating a digital behavioral health intervention to improve bladder-filling compliance in patients with prostate cancer receiving radiation.

70 Background: Prostate cancer (PCa) patients undergoing radiation therapy (RT) need comfortably full bladders during RT to reduce toxicities. Awareness of this state is lacking, and poor compliance is common with standard of care written/verbal instructions, leading to wasted patient value (PV) and inefficient throughput (TE). Herein, we assessed the feasibility and acceptability of a digital behavioral intervention (DBI) to improve bladder filling compliance, methods for quantifying PV, IT, and behavioral biomarkers. Methods: We enrolled 18 non-metastatic PCa patients in a prospective, single-arm pilot study using a DBI. The DBI consists of a smart water bottle and companion app that tracks water consumption. Additionally, visual alerts (water bottle glow and phone notifications) remind the patient to empty his bladder and drink a personalized volume goal (VG) at a set time. The VG is based on simulation bladder volume and reminders are initially set 1.25 hours prior to scheduled RT. Patients were trained to adjust their VG and notification times to achieve comfortably full bladders. Primary endpoint was met if qualitative (QLC) and quantitative compliance (QNC) were both &gt;80%. Other endpoints include acceptability assessed via Service User Technology Acceptability Questionnaire (SUTAQ), engagement (&gt;80% of patients used the device &gt; 50% of the time) and impact of DBI on PV and TE. For QLC, patients were asked if they prepared their bladders before daily RT. QNC was met if bladder volumes on daily CBCT were &gt; 75% of the simulation’s volume. The SUTAQ was given pre- and post-DBI. PV and TE were measured by time spent in clinic and on the linear accelerator (linac), respectively. Biometric data was probed for insights on optimal drinking behaviors. Results: All endpoints were met. QLC was 100% on 375/398 (94.2%) of total treatments, while QNC was 88.9% on 341/398 (85.7%) total treatments. Patients were accepting of DBI with few privacy concerns (4.33/5.00), believe in benefits (4.00/5.00), high satisfaction (4.56/5.00) and high usability (4.24/5.00). Most patients, 15/18 (83.3%), used the DBI on &gt;50% of treatments and met the engagement requirements. Patients with empty bladders (n=43) spent significantly more time (75.14 vs 50.59 minutes, p=0.007) in clinic than patients who came with full bladders (n=355). Similarly, these patients spent nearly twice as long on the linac (21.63 v 12.50 minutes, p&lt;0.001). Shorter time spent drinking correlated with empty bladders (17.07 vs 27.85 min, p=0.027). Conclusions: This digital intervention trial showed high rates of bladder filling compliance and engagement. High patient-value and throughput efficiency was feasibly quantified by shortened clinic times and linac usage, respectively. Future studies are needed to evaluate clinical outcomes, patient experience and cost benefit. Clinical trial information: NCT04946214 .

Lean Laboratory—Designing an Application of Lean for Teaching and Research Laboratories

Although Lean Thinking (LT) is becoming popular in service organizations, few studies have applied Lean tools to the education sector. Furthermore, although inventories are one of the main wastes in this sector, to date, no study has presented a decision support system (DSS), based on LT, for stock management in academic institutions. This study aims to design an application of LT in teaching and research laboratories to provide improvements in workplace organization, setup times, and stock management. Lean tools were implemented in a case study, and an e-Kanban DSS was designed to determine when and in what quantity an order should be placed, considering factors such as forecasting demand, demand level (high or low), and component deterioration. For testing the DSS, a simulation was carried out using as an example a set of articles that integrate the protocol of laboratory activity. The implementation of Lean tools improved the organization of the workplace, which contributed to a reduction in unnecessary movements and the setup time of practical activities. Additionally, the study demonstrated that the developed DSS calculates the quantities to be ordered according to the selected mathematical formulas and generates appropriate visual alerts, depending on the need to place an order.

A Low-Cost Wearable Autonomous System for the Protection of Bicycle Users

A bicycle is a form of transport that not only positively impacts the health of users, and the general population by reducing pollution levels but also constitutes an accessible and affordable means of transport for developing societies. However, when coexisting with other forms of transport, the accident rate is elevated, and the risk is high. Among the factors contributing to accidents involving bicycles are collisions with motor vehicles. These accidents can occur when a motor vehicle maneuvers and does not see the bicycle or when a motorist drives distracted. These types of accidents can be avoided if cyclists and motorists are aware of the environment and respect traffic laws and safety regulations. This research aims to develop a low-cost autonomous electronic system that provides extra protection to bicycle users, particularly by making them visible to other road users on cloudy days or at night. The system uses a 32-bit processor with brightness and acceleration sensors that trigger visual alerts to both the bicycle user and possible nearby vehicles. It also monitors and logs the signals on a server for route evaluation. The laboratory successfully evaluated the prototype, demonstrating its autonomy and performance. The test results obtained demonstrate the system’s capacity to provide extra protection, in addition to its robustness and accuracy.

The Stress of Vigilance May Influence Reports of Simulator Sickness: A Factor Analysis of the SSSQ and SSQ in Auditory Vigilance Tasks

This study investigated the relationship between stress and simulator sickness within auditory vigilance tasks as a follow-up to reports that stress was linked to simulator sickness in visual vigilance tasks. The Simulator Sickness Questionnaire (SSQ) and the Short Stress State Questionnaire (SSSQ) were employed in two different experiments utilizing auditory vigilance tasks. Correlational relationships between reported stress and simulator sickness were investigated using exploratory factor analysis. These results support findings that the stress of vigilance is associated with reports of simulator sickness in vigilance tasks. Since simulator sickness should not occur within an auditory task due to a lack of visuo-vestibular conflict, these findings also support the notion that the stress of vigilance exaggerates reports of simulator sickness symptomology. These stress-related reports of simulator sickness symptomology in auditory vigilance are a potential concern for the validity of simulator sickness assessments within tasks that require vigilance.

Modeling and insights into the structural basis of chemical acute aquatic toxicity

It has become a top global regulatory priority to prevent and control pollution from the release of synthetic chemicals, which continues to affect the aquatic communities. In the past decades, computational tools were largely used to significantly reduce the budget and time cost of chemical acute aquatic toxicity assessment. But the structural basis of toxic compounds was rarely analyzed. In the present study, we collected 1438, 485 and 961 chemicals with acute toxicity data records for three representative aquatic species, including Tetrahymena pyriformis, Daphnia magna, and Fathead minnow, respectively. A series of artificial intelligence models were developed using OCHEM tools. For each aquatic toxicity endpoint, a consensus model was developed based on the top performed individual models. The consensus models provided good performance on external validation sets with total accuracy values 96.88 %, 90.63 %, and 84.90 % for Tetrahymena pyriformis toxicity (TPT), Daphnia magna toxicity (DMT), and Fathead minnow toxicity (FMT), respectively. The models can be freely accessed via https://ochem.eu/article/146910. Moreover, the analysis of physical-chemical properties suggested that several key molecular properties of aquatic toxic compounds were significantly different with those of non-toxic compounds. Thus, these descriptors may be associated to chemical acute aquatic toxicity, and may be useful for the understand of chemical aquatic toxicity. Besides, in this study, the structural alerts for aquatic toxicity were detected using f-score and frequency ratio analysis of predefined substructures. A total of 112, 58 and 33 structural alerts were identified responsible for TPT, DMT, and FMT, respectively. These structural alerts could provide useful information for the mechanisms of chemical aquatic toxicity and visual alerts for environmental assessment. All the structural alerts were integrated in the web-server SApredictor (www.sapredictor.cn).