Distraction Behavior Research Articles

European NCAP Driver State Monitoring Protocols: Prevalence of Distraction in Naturalistic Driving.

examine the prevalence of driver distraction in naturalistic driving when implementing European New Car Assessment Program (Euro NCAP)-defined distraction behaviours. The 2023 introduction of Occupant Status monitoring (OSM) into Euro NCAP will accelerate uptake of Driver State Monitoring (DSM). Euro NCAP outlines distraction behaviours that DSM must detect to earn maximum safety points. Distraction behaviour prevalence and driver alerting and intervention frequency have yet to be examined in naturalistic driving. Twenty healthcare workers were provided with an instrumented vehicle for approximately two weeks. Data were continuously monitored with automotive grade DSM during daily work commutes, resulting in 168.8 hours of driver head, eye and gaze tracking. Single long distraction events were the most prevalent, with .89 events/hour. Implementing different thresholds for driving-related and driving-unrelated glance regions impacts alerting rates. Lizard glances (primarily gaze movement) occurred more frequently than owl glances (primarily head movement). Visual time-sharing events occurred at a rate of .21 events/hour. Euro NCAP-described driver distraction occurs naturalistically. Lizard glances, requiring gaze tracking, occurred in high frequency relative to owl glances, which only require head tracking, indicating that less sophisticated DSM will miss a substantial amount of distraction events. This work informs OEMs, DSM manufacturers and regulators of the expected alerting rate of Euro NCAP defined distraction behaviours. Alerting rates will vary with protocol implementation, technology capability, and HMI strategies adopted by the OEMs, in turn impacting safety outcomes, user experience and acceptance of DSM technology.

Driver Attention Detection Based on Improved YOLOv5

In response to negative impacts such as personal and property safety hazards caused by drivers being distracted while driving on the road, this article proposes a driver’s attention state-detection method based on the improved You Only Look Once version five (YOLOv5). Both fatigue and distracted behavior can cause a driver’s attention to be diverted during the driving process. Firstly, key facial points of the driver are located, and the aspect ratio of the eyes and mouth is calculated. Through the examination of relevant information and repeated experimental verification, threshold values for the aspect ratio of the eyes and mouth under fatigue conditions, corresponding to closed eyes and yawning, are established. By calculating the aspect ratio of the driver’s eyes and mouth, it is possible to accurately detect whether the driver is in a state of fatigue. Secondly, distracted abnormal behavior is detected using an improved YOLOv5 model. The backbone network feature extraction element is modified by adding specific modules to obtain different receptive fields through multiple convolution operations on the input feature map, thereby enhancing the feature extraction ability of the network. The introduction of Swin Transformer modules in the feature fusion network replaces the Bottleneck modules in the C3 module, reducing the computational complexity of the model while increasing its receptive field. Additionally, the network connection in the feature fusion element has been modified to enhance its ability to fuse information from feature maps of different sizes. Three datasets were created of distracting behaviors commonly observed during driving: smoking, drinking water, and using a mobile phone. These datasets were used to train and test the model. After testing, the mAP (mean average precision) has improved by 2.4% compared to the model before improvement. Finally, through comparison and ablation experiments, the feasibility of this method has been verified, which can effectively detect fatigue and distracted abnormal behavior.

From motivational experience to creative writing: A motivational AR-based learning approach to promoting Chinese writing performance and positive writing behaviours

The ability to write is one of the key literacies required by students in relation to 21st century skills and is an emphasis of early education. Although the ability to write is a vital foundation for further education, due to the lack of authentic experience and limited instructional methods, cultivating writing ability in the early stage of education is still a significant challenge for students in elementary schools. Therefore, the majority of students are not able to actively engage in the process of learning to write. To address these problems, this study proposes a motivational AR-based (MAR) learning approach to support students in learning to write, which not only provides an authentic learning environment, but also aims to motivate students' active writing. Its effects were verified through a quasi-experiment. A total of 47 elementary school students from China were invited to participate, and were assigned to either an experimental group (EG) which was exposed to the MAR approach or the control group (CG) which was exposed to the motivational learning approach in a conventional environment (MC). The results showed that the proposed MAR learning approach improved the students' writing performance in terms of feature descriptiveness and thinking innovation. It can be found that, compared with the CG, the achievement gaps between the low- and high-engagement students were much larger in the EG. In addition, the sequential pattern analysis results showed that students who learned with the MAR learning approach concentrated more on the process of observation than those who learned with the MC learning approach, and they tended to exhibit less distracted behaviours. To sum up, the proposed MAR learning approach was effective in terms of facilitating elementary school students’ writing education. The main contribution of our study is that it provides evidence for the effectiveness of the proposed MAR learning approach and opens up opportunities for future studies which aim to further explore its impacts in different designs of writing learning activities.

Incorporating the Theory of Planned Behaviour into Distracted Driving: Influencing Factors and Intervention Effects

This study focuses on the psychological characteristics and empirically tests of the factors influencing distracted driving behaviours. This information is used as a reference for an intervention on dangerous driving behaviours. First, a distracted driving scale is constructed based on the theory of planned behaviour (TPB). The questionnaires are distributed in Chongqing, China, and 321 completed questionnaires are obtained. Data are analyzed using mean-variance analysis, one-way ANOVA, T-test, and multivariate test by SPSS 26.0 to determine the significance of distracted behaviours and demographic variables. We use a structural equation model to determine the path coefficients of each latent variable. Finally, we select the drivers with high tendency of distraction from the results of the questionnaires, conduct a four-stage rational emotional behaviour therapy (REBT) experiment, and use a repeated measures ANOVA analysis to test the validity and persistence of the intervention method. Results show that subjective norm is the most influential psychological factor. There are significant differences between the experimental group (2.38, SD = 0.41) and the control group (2.89, SD = 0.40) in the scores of distractions. This indicates that the distracted behaviour intervention achieves adequate validity and consistency. Educational research on distracted driving behaviour can help identify and correct drivers with high distraction tendency.

Distraction pattern classification and comparisons under different conditions in the full-touch HMI mode

Understanding driver distraction patterns is an important part of human–machine interaction (HMI), which is beneficial for the development of control strategies in human–machine co-driving systems. However, comparatively few studies have focused on driver distraction patterns. To address this issue, this study proposes a framework to characterize distraction patterns using glance behavior and manual behavior, and classifies distraction patterns into: aggressive, moderate, and conservative patterns based on real road experiments. Subsequently, differences in distraction behavior and effects on lateral vehicle control ability across distraction pattern groups, as well as distraction behavior differences exhibited by drivers in the same distraction pattern group under different conditions, are analyzed. Firstly, the results show that the aggressive distraction patterns have a smaller number of eyes-off-road (NoEOR) incidences but longer mean single eyes-off-road time (MSEORT), maximum single eyes-off-road time (MaxEORT) and a higher percentage of long eyes-off-road (PoLEOR) incidences than the other patterns. There are slight differences in the single eyes-off-road times (EORTs) between the conservative and moderate patterns and in the manual behavior for the aggressive and moderate distraction patterns. Secondly, the same distraction pattern exhibited by drivers for different road and secondary task conditions has differences in terms of the behavioral performance. Finally, there is few differences in the lateral motion of a vehicle with different distraction patterns. Surprisingly, the standard deviation of the steering wheel angle (SDSWA) is the smallest in the aggressive distraction pattern.

Measurements of classroom acoustic conditions in which young neurodiverse persons learn in São Paulo and Nebraska

Autism is a set of complex neurobehavioral conditions that involve, among other characteristics, hypo/hypersensitive responses to any sensory input, usually referred to as sensory processing disorder. The most common sensory difference associated with autistic individuals is an over or under response to visuals, touch, smells, tastes, balance, and sounds when compared to neurotypical individuals. Among these, auditory sensitivity has a significant influence. Research studies to date have revealed that autistic individuals are highly susceptible to surrounding sounds, and show that certain noises, such as loud sounds, classroom bells, sirens, the radio or the TV, and traffic noise, may induce uncomfortable and distracting behavior. In this study, we investigated the acoustic environment of classrooms/facilities designed for a more inclusive education. A number of room acoustic descriptors, such as reverberation time (RT), speech clarity (C50), speech transmission index (STI), sound levels, and auditory strength (S/N ratio) were measured in this regard. The experimental measurements were performed in several rooms and facilities focusing on autistic individuals in Campinas, São Paulo, in Brazil and in Omaha, Nebraska, in the United States.

Analysis of Driver Distraction Behaviour Causing Risk of Accidents in Jordan

This study investigates into the underlying causes of distracted driving leading to highway collisions among Jordanian drivers aged 18 to 65. Surveys unveiled a concerning statistic, indicating that a notable 64% of drivers involved in crashes were distracted. Among the identified distractions, texting emerged as the most perilous, with mental distractions such as anger or sadness ranking as the primary cause of crashes (20%), followed by interactions with passengers (13%) and phone conversations (11%). Furthermore, the study revealed a disproportionate representation of male drivers and individuals from both younger and older age groups in distracted driving incidents. These findings underscore the imperative for targeted interventions aimed at curbing distracted driving behaviors across diverse demographic segments, thus fostering safer road environments.

Effectiveness of Applied Behavior Analysis Therapy Against Decreased Distracted Behavior in Children Attention Deficit Hyperactive Disorder

Epidemiological data indicate that ADHD (Attention Deficit Hyperactive Disorder) affects the population of children and young adults worldwide by 3.4%. Difficulty in focusing attention is accompanied by an increase in excessive motor activity, one of which is overcome by using the ABA (Applied Behavior Analysis) method of therapy. The purpose of this study is to determine whether ABA therapy reduces distracted behavior in children with ADHD using an experimental design in one participant. Subjects have criteria, namely, children aged 5-6 years and experiencing ADHD problems. This exploration information assortment strategy as perception, meetings, and documentation. The aftereffects of the review expressed that ABA therapy was effective in reducing distracted behavior in children with ADHD. The subject experienced a decrease in distracted behavior withha significance value of 0,043 (p<0,05). Starting from the beginning before being given treatment or the pre-test stage, the frequency of distraction was 112 times, compared to the final stage after being given treatment, the frequency of distraction was 30 times. Data epidemiologi mengindikasikan bahwa ADHD (Attention Deficit Hyperactive Disorder) mempengaruhi populasi anak dan dewasa muda diseluruh dunia sebesar 3,4%. Kesulitan dalam memusatkan perhatian disertai dengan meningkatnya aktivitas motorik yang berlebihan salah satunya diatasi dengan terapi ABA (Applied Behavior Analysis). Tujuan penelitian ialah untuk mengetahui efektivitas terapi ABA terhadap penurunan perilaku terdistraksi pada anak ADHD dengan menggunakan experimental design pada satu partisipan. Subjek memiliki kriteria yaitu, anak berusia 5-6 tahun dan mengalami permasalahan ADHD. Metode pengumpulan data menggunakan observasi, wawancara dan dokumentasi. Hasil penelitian menyatakan bahwa terapi ABA efektif untuk mengurangi perilaku terdistraksi pada anak dengan gangguan ADHD. Subjek mengalami penurunan perilaku terdistraksi dengann nilai signifikansi 0,043 (p<0,05). Terhitung mulai dari awal sebelum diberikan perlakuan atau tahap pre-test frekuensi terdistraksi sebesar 112 kali, dibandingkan dengan tahap akhir setelah diberikan perlakuan frekuensi distraksinya sebanyak 30 kali.

Institutional Investors’ Distraction and Executive Compensation Stickiness Based on Multiple Regression Analysis

Based on the impact of industry extreme return on the attention of institutional investors, taking Chinese A-share listed companies from 2011 to 2020 as a sample, this paper empirically tests the relationship between institutional investors’ distraction and executive compensation stickiness based on multiple regression analysis. The study finds that institutional investors’ distraction promotes the executive compensation stickiness, which is more significant in the group of pressure-resistant institutional investors. The mechanism test finds that based on the governance effect, information effect and psychological effect, corporate external governance, stock price information content and management anxiety play a partial intermediary role between institutional investors’ distraction and executive compensation stickiness. The moderating effect finds that the level of corporate internal governance and managerial overconfidence will weaken the impact of institutional investors’ distraction on executive compensation stickiness. In addition, the distraction behavior in non-state-owned and western companies has a more significant economic impact.

An Epidemiological Study of Cell Phone-Related Injuries of the Hand and Wrist Reported in United States Emergency Departments From 2011 to 2020

Increasing ownership and use of mobile phones has been recently linked to reports of hand and wrist pain from overuse, as well as more serious injuries related to distracted behaviors, such as falls and texting while driving. We describe the epidemiology of hand and wrist injuries presenting to US emergency departments from 2011 to 2020, which were associated with cell phone use using the Consumer Product Safety Commission's National Electronic Injury Surveillance System. The National Electronic Injury Surveillance System database was queried for treatment records from 2011 through to 2020 for all cell phone-related injuries of the lower arm, wrist, hand, and fingers. Using parameters provided by the National Electronic Injury Surveillance System database, there were 1,213 unique cases, yielding a total weighted estimate of 50,487 national cases presenting to emergency departments in the United States. Between 2011 and 2020, the weighted estimate of annual cell phone-related injuries ranged from 3,389 to 7,320 cases. Falls were the most common cause of injury, accounting for 29.8% of estimated cases. The most common types of injuries were lacerations (22.3%). The national estimate of cell phone-related injury was the highest in the age range of 11-20 years (26.4%), followed by 21-30 years (22.2%). Women were affected more frequently than men (59.6% vs 40.4%). Upper extremity injuries related to cell phone use represent an increasing burden of disease to the US healthcare system. Raising awareness regarding cell phone-related injuries and in-home fall-prevention strategies, especially among elderly individuals, should be considered as means of decreasing the number of such injuries. Strategies for decreasing the burden of cell phone-related injuries occurring as a result of falls among teenagers and young adults should focus on minimizing distractions while using a cell phone. Limitations of the study include inaccuracies related to probability-weighted case estimation and limitations in reporting injuries. Knowledge of the burden of upper extremities injuries associated with this common handheld device can help to both raise awareness of this issue, as well as to potentially inform injury-prevention strategies.

IoT-Based Non-Intrusive Automated Driver Drowsiness Monitoring Framework for Logistics and Public Transport Applications to Enhance Road Safety

The exponential growth in road accidents has led to a need for continuous driver monitoring to enhance road safety. Existing techniques rely on vehicle sensor-based and behavior analysis-based approaches, where the behavior analysis-based approaches are generally considered more desirable as they enable reliable detection of a more elaborate set of driver behaviors. They are categorized as intrusive and non-intrusive approaches. Unlike intrusive approaches that generally rely on constant direct human contact with sensors (physiological signals) and are sensitive to artifacts, non-intrusive approaches offer a more effective behavior monitoring using computer vision-based techniques. This paper proposes an end-to-end non-intrusive IoT-based automated framework to monitor driver behaviors, designed specifically for logistic and public transport applications. It consists of an embedded system, edge computing and cloud computing modules, and a mobile phone application, in an attempt to provide a holistic unified solution for drowsiness detection, monitoring, as well as evaluation of drivers. Drowsiness detection is based on detecting sleeping, yawning, and distraction behaviors using an image processing-based technique. To minimize the effects of latency, throughput, and packet losses, edge computing is performed using commercial off-the-shelf embedded boards. Moreover, a cloud-hosted real-time database for remote monitoring on interactive Android mobile application has been set up, where admin can add multiple drivers to get drowsiness notifications along with other useful related information for driver evaluation. An extensive experimental testing has been performed, obtaining encouraging results. An overall accuracy of 96% is achieved along with an enhanced robustness, portability, and usability of the proposed framework.

Comparative evaluation of anxiety level during restorative treatment using no music, monaural beats, and binaural auditory beats as audio distraction behavior guidance technique in children aged 6-12 years: A randomized clinical trial.

Binaural auditory beat is an auditory illusion perceived when two different pure-tone sine waves with less than a 30 Hz difference are presented to a listener dichotically. Monaural beat stimulation is achieved by the superposition of amplitude-modulated signals of nearby frequencies to both ears simultaneously. The study aims to evaluate and compare anxiety level during restorative treatment using no music, monaural beats, and binaural auditory beats as audio distraction behavior guidance techniques in children aged 6-12 years. Three-arm, parallel-group randomized study was conducted in the department of pediatric and preventive dentistry. The sample size of 45 participants was calculated and allocated into three groups as no music, monaural beats, and binaural auditory beats group. Teeth with Class I cavity in primary or permanent molars were prepared and restored. Anxiety was measured pre and postintervention using pulse oximeter and Venham's picture test. Data obtained were analyzed with SPSS software version 20.0 and were carried out using one-way analysis of variance and dependent t-test and significance value < 0.05 was considered. Binaural auditory beats and monaural beats showed a better reduction in anxiety levels as per Venham's picture test scores with statistically significant "P" value 0.0001 and 0.064, respectively. Likewise, binaural auditory beats showed better reduction in anxiety levels as per pulse rate scores with statistically significant "P" value 0.0001 as compared to conventional treatment technique. Binaural auditory beat was found to be the most effective followed by the monaural beat compared to the control group. Thus, binaural auditory beats and monaural beats can be used effectively as an essential aid to reduce anxiety in children.

Analysis of mobile phone use engagement during naturalistic driving through explainable imbalanced machine learning

While driver distraction remains an issue in modernized societies, technological advancements in data collection, storage and analysis provide the means for deeper insights of this complex phenomenon. In this research, factors influencing when driver distraction through mobile phone use occurs during naturalistic driving are investigated. Naturalistic data from a 6-stage, 230-driver experiment are exploited, in which drivers installed a non-intrusive driving recording application in their devices and conducted their trips normally across a 21-month timespan, coupled with corresponding questionnaire data. The various experiment stages involved providing progressively more behavioral feedback to drivers while continuing to record them. Subsequently, supervised Machine Learning XGBoost algorithms were employed to model the contributions of naturalistic driving and questionnaire features to the decision to engage mobile phone use. Mobile phone use percentages were heavily skewed towards zero, therefore imbalanced ML with a minority-oversampling approach in a binary format was employed. To increase the explainability offered by the algorithm, SHAP values were calculated for the informative features. Results indicate that the decision of drivers to use a mobile while driving is governed by a number of complex, non-linear relationships. Total trip distance is the most significant predictor variable by a wide margin, with mean SHAP values of 0.79 towards affecting the model decisions for the probability of mobile phone use of each driver. However, other variables influence the final predictions as well, such as the number of tickets in the last three years (m.SHAP = 0.30), declared mobile phone use (m.SHAP = 0.26), the amount and variety of provided feedback (m.SHAP = 0.17) (i.e. experiment phase number) and family member numbers (m.SHAP = 0.09) decrease the probability of using a mobile phone while driving. Conversely, increases in driver experience (m.SHAP = 0.22), driver age (m.SHAP = 0.11), engine capacity (m.SHAP = 0.11) and total kilometers driven annually (m.SHAP = 0.08) increase the probability of using a mobile phone in naturalistic driving conditions. SHAP dependency plots reveal non-linear effects present in almost all variables. Fuel consumption had a particularly strong non-linear effect, as higher values of this variable lead to both higher and lower probability of drivers using a mobile phone, deviating from the safer average. Legislation, campaigns and enforcement measures can be restructured to take advantage of gains margins in terms of understanding and predicting driver distraction behavior, as explored in the present study.

GUIDELINES FOR DEVELOPING EVIDENCE-BASED RISKY DRIVING COUNTERMEASURES THAT INCLUDE OLDER DRIVERS

Abstract Driver behavior will continue to play a critical role in driving safety for the foreseeable future. Utilizing behavior change theory appropriately presents opportunities to improve the effectiveness of risky driving countermeasures that have been under-utilized to date. Older drivers should not be excluded from consideration of risky behaviors. Forty-six drivers (33% age 65+) completed surveys, then drove for three weeks with data collection during all trips. The Theory of Planned Behavior guided a two-phased regression analysis approach: 1) behavioral intentions were predicted using attitudes about behaviors and demographics; 2) observed risky behavior was predicted using behavioral intentions, theory constructs, personality/psychosocial characteristics, demographics, and driving exposure. Results were synthesized and the emergent themes were used to formulate guidelines for developing theory-based education and communication risky driving countermeasures. Guidelines focused on four risky driving behaviors observed in a large proportion of participants (72% - 96%): holding/using a cellphone; eating/drinking; speeding; and tailgating. Twenty-six guidelines were developed across four categories: 1) relationships among risky behaviors; 2) characteristics or underlying dimensions of risky driving (e.g., time, location, emotion); 3) behavior change theory constructs; 4) audience and message factors. While older drivers self-reported low frequencies of risky behaviors, low intentions for future risky behaviors, and less favorable attitudes toward risky behaviors than younger drivers they were regularly observed engaging in risky behaviors: distracted behaviors in 79% of trips and 2.1 speeding events per trip. Risky driving countermeasures are appropriate for older drivers and the emergent guidelines will be presented with recommended variations for older drivers.

Detection and Recognition of Driver Distraction Using Multimodal Signals

Distracted driving is a leading cause of accidents worldwide. The tasks of distraction detection and recognition have been traditionally addressed as computer vision problems. However, distracted behaviors are not always expressed in a visually observable way. In this work, we introduce a novel multimodal dataset of distracted driver behaviors, consisting of data collected using twelve information channels coming from visual, acoustic, near-infrared, thermal, physiological and linguistic modalities. The data were collected from 45 subjects while being exposed to four different distractions (three cognitive and one physical). For the purposes of this paper, we performed experiments with visual, physiological, and thermal information to explore potential of multimodal modeling for distraction recognition. In addition, we analyze the value of different modalities by identifying specific visual, physiological, and thermal groups of features that contribute the most to distraction characterization. Our results highlight the advantage of multimodal representations and reveal valuable insights for the role played by the three modalities on identifying different types of driving distractions.

Effects of members’ response styles in an online depression community based on text mining and empirical analysis

The online depression community (ODC) has become a popular resource for people with depression to manage their mental health during the COVID-19 pandemic. This study proposed a novel perspective based on response style theory to investigate whether depression individuals’ distractive and ruminative behaviors in ODC were related to social support received and co-rumination. Furthermore, we explored the influences of social support and co-rumination on suicidal behaviors using panel data set. We collected text data from 22,286 depressed users of a large ODC in China from March 2020 to July 2021, and conducted text mining and econometrics analyses to test our research questions. The results showed that depression users’ online ruminative behaviors had a positive relationship with the co-rumination and had a negative relationship with social support received. Besides, constructive distractive behaviors (i.e., providing social support to others) increased the support users received from others but had a negative relationship with co-rumination. Depression users' future suicidal behaviors are influenced by past received social support and co-rumination. The received social supports and co-rumination have a negative and positive influence on depression users' future suicidal behaviors, respectively. Our results enrich the application of response style theory in online medicine. They provide meaningful insights into behaviors that influence the acquisition of online social support and the incidence of online co-rumination in ODCs. This study helps relevant institutions to conduct more targeted online suicide interventions for depression patients.

Foster children avoidance-related interactive behaviors with caregivers

BackgroundA lot of the research concerning foster children – often children who have suffered maltreatment in the family home - has focused on internalized and externalized symptoms. Few studies, however, have looked at the interactions between such children and caregivers. PurposeThe purpose of this study is to explore the Emotion Regulation Strategies (ERS) of children in foster care and to highlight those most commonly employed in family or placement contexts. The parents' and foster carers' ERS are also analyzed in order to understand the co-regulatory processes at work. MethodAn in-depth analysis of observation sequences was performed. Three data collection times, spaced across a period of 6 months (t1, t2 and t3), were included in the observation protocol. Each observation, recorded using a video camera, comprised 45 min of free time and 15 min of structured tasks. Transcription and coding of ERS were performed for each sequence using a microanalytical method. Both children's and adults' ERS were coded. ResultsChildren tended to be readily distracted when interacting with adults, and more particularly so with their parents. While they tended to display relatively normative processes with a foster carer, they turned to pathological avoidance mechanisms with their parents such as physical venting or self-stimulation. Interactions during structured tasks showed a significant reduction in distraction processes. ConclusionThis study highlights the prevalence of distraction behavior in foster children during their interactions with caregivers, and offers an insight into how structured interactions provide a framework that mitigates children's avoidance behaviors and so enhances adult-child collaboration.

Mental models of driver monitoring systems: Perceptions of monitoring capabilities in an online U.S-based sample

Driver monitoring, whether accompanying vehicle automation or not, appears poised for wide deployment in the near future. Driver monitoring is itself a form of automation (often needed in response to having automated other aspects of driving), and previous automation research has indicated that accurate mental models are critical to successful human-automation interactions. In this study, a descriptive survey of car owners examined perceived capabilities of generic driver monitoring systems (DMS) to detect 30 different potentially distracting behaviors or adverse mental states. Additional open-ended questions queried drivers about beliefs regarding how DMS work and how they might be defeated. Exploratory factor analysis revealed that perceived capabilities factored into four categories, including beliefs that driver monitoring systems can detect: (1) console and control interactions; (2) smartphone interactions; (3) gaze position and in-cabin movement; and (4) adverse mental states. Although research and industry analysis have suggested that camera-based gaze monitoring will be the path forward, a considerable minority of drivers appear to believe that console interactions will be monitored. Further, car owners in this study generally were skeptical about the ability DMS to detect adverse mental states, which may be in misalignment with the capabilities of systems that will be available in the near future. For future research, questions remain about the potential implications of false alarms (real and perceived) in driver monitoring, as well as the role of experience and initial training/instruction for deployed systems.

Do Practice Policies Limit Distractions and Improve the Quality During an Obstetric Sonogram?

Objective: To assess if distractions alter sonographers’ perceptions of an obstetric sonogram’s (OBS) quality and whether clinic policies mitigate this association. Materials and Methods: Members of the Society of Diagnostic Medical Sonography were surveyed regarding demographics, perceived distractions during OBS, clinic observer and/or recording policies, plus perceptions of OBS quality concerns. Distraction and OBS quality scores were calculated. Chi-squared tests were used for bivariate analysis. Logistic regression identified independent predictors of OBS quality concerns. Results: There were 805 surveys completed. Many respondents (68%) found more than 75% of behaviors distracting, which was associated with increased reports of OBS quality concerns (adjusted odds ratio [aOR] = 3.63, 95% confidence interval [CI]: 2.55, 5.15), despite respondents’ demographics or clinic policies. More distracting behaviors (79%, P &lt; .01) and quality concerns (40%, P = .04) were noted by sonographers who wanted, but did not have, recording policies at their clinic. Increased quality concerns correlated with disagreement concerning current recording policies (aOR = 2.89, 95% CI: 1.11, 8.00) and desired but absent observer policy (aOR = 2.24, 95% CI: 1.09, 4.59), regardless of respondent demographics or distraction score. Conclusion: Increased distraction scores are associated with more sonographer reports of OBS quality concerns. The lack of desired policies was associated with increased distractions and quality concerns. Further work is needed to understand the relationship between clinic policies, perceived distraction, and OBS quality.

Unsupervised Sparse, Nonnegative, Low Rank Dictionary Learning for Detection of Driver Cell Phone Usage

One of the most significant problem in traffic accidents is distracted driving that causes a higher number of deaths and injury every year. In recent times car manufacturing companies are integrating driver distraction detection systems for detecting distracted behavior. In this work we have suggested an mobile usage detection system in driving images taken from inside of a car. Previously proposed distracted driver detection systems that focus only on gaze and head poses. We have suggested another alternative using driver hand images by which distracted driving detection system can be benefited. If the driver looks at the road ahead, previous head pose and gaze based systems are unable to detect distractions caused by cell phone use. To reliably detect these modes of distraction, we trained an unsupervised low rank nonnegative dictionary and used a threshold based criterion over reconstruction error. We have created a new driving dataset based on YouTube videos that includes real-world driving scenarios as well as mobile phone usage while driving. We have also tested our method using a number of publicly available driving datasets, including the State farm driving dataset, the AUC driving dataset, the VIVA dataset, and the CVRR-3D dataset. According to our knowledge of the current literature, this method is the first to use an unsupervised approach to address the problem of cell phone usage while driving. We achieved a competitive test accuracy of 79.17%, 77.19%, 84.0%, 76.0%, 75.45% in the proposed Youtube ego driving, State farm driving, CVRR-3D, VIVA, AUC distracted driver dataset, respectively.