Video-based Approach Research Articles

SHEEPFEARNET: Sheep fear test behaviors classification approach from video data based on optical flow and convolutional neural networks

Determining the temperament related traits of sheep, such as the coping style with various stress factors such as people, a new environment and social isolation, is essential in terms of improving animal welfare and increasing productivity. The classification of sheep according to their behavioral responses to the mentioned stress factors is evaluated by objective or subjective methods by expert observers. However, visual examinations that rely on human observation are more likely to make mistakes and are time consuming. Therefore, it is important to make this process faster, easier and more reliable. The phenotypic and genetic heritability of temperament traits in sheep are examined using behavioral tests such as arena and isolation box. The spatial features of the temperament classes in these tests are generally similar. At the same time, since the behavior traits are composed of time series, defining or classifying these features with image-based approaches can present challenges. In this study, we propose a video-based approach to overcome this challenge, using videos of behavioral traits obtained from fear tests. In this approach, we used a combination of optical flow for capturing temporal features and convolutional neural networks for capturing spatial features. The experimental results show that, balanced datasets in terms of the number of sheep, the BOF-VGG19 model trained with the transfer learning method is 90%, the BOF-CovnLSTM model using ConvLSTM networks is 95%, and the BOF-CNN model using CNNs is 100%, were determined as the optical flow models that classify fear test behavior traits the most successfully. The success rate of UNB-CNN and B-CNN models trained on raw images was 70%. As a result, we obtained successful results in classifying behavioral traits in models trained with optical flow pre-processed data sets balanced in terms of sheep numbers. At the same time, using a combination of optical flow and convolutional neural networks in videos where spatial features between temperament classes are similar enhanced the classification accuracy of fear behavior traits by capturing temporal features.

Effects of technologically-rich physics homework: findings from an experimental study

Students’ learning outcomes in physics are significantly affected by the quality of outside-of-classroom learning experiences. A rich source of these experiences may be the physics homework. In this study, the effects of technologically-rich physics homework were evaluated. To that end a pretest-posttest experimental design has been used. 67 first-year students from the First Bosniak Gymnasium were randomly assigned to one of the three homework approaches. In the control group students received conventional homework about work and energy. Within the simulation-based approach students were expected to interact with simulations to investigate work and energy phenomena, whereas in the video-based approach students learned by analyzing a video in which the teacher interacted with the same simulations as mentioned above. Based on analysis of covariance we could find that the between-group differences on the conceptual posttest were not statistically significant, F(2, 47)=0.59, p=0.56. At the same time, students who learned by interacting with simulations expressed significantly more positive attitudes towards homework, compared to students from the video-based group (mean difference=1.88, p=0.038), as well as compared to students from the conventional group (mean difference=2.02, p=0.03). Simulation-based physics homework may be a powerful tool for helping the students to reach important affective learning outcomes.

An Experiential Learning-Based Virtual Reality Approach to Foster Students’ Vocabulary Acquisition and Learning Engagement in English for Geography

EFL learners encounter a number of challenges in English for specific purposes, especially in understanding and remembering vocabulary. Therefore, exploring effective ways to improve their vocabulary and its applications is the key area of ESP. VR, as a newer type of audiovisual input on incidental vocabulary learning, is an important tool for innovation in language education. With its sense of presence and immersion, VR constructs an experiential learning process for students involving incidental vocabulary acquisition to promote their learning engagement and performance. Therefore, this study applied an experiential learning-based VR approach to an English for Geography course in a university. Thirty-six geography students participated in vocabulary acquisition on the theme of the hydrologic cycle. For the experiment, 18 students were assigned to the experimental group learning with the VR-based approach, while the other 18 were assigned to the control group learning with the video-based approach. The findings demonstrated that the experimental group outperformed the control group in terms of incidental vocabulary acquisition and cognitive, behavioral, and social engagement.

Video-Based Hand Movement Analysis of Parkinson Patients before and after Medication Using High-Frame-Rate Videos and MediaPipe.

Tremor is one of the common symptoms of Parkinson’s disease (PD). Thanks to the recent evolution of digital technologies, monitoring of PD patients’ hand movements employing contactless methods gained momentum. Objective: We aimed to quantitatively assess hand movements in patients suffering from PD using the artificial intelligence (AI)-based hand-tracking technologies of MediaPipe. Method: High-frame-rate videos and accelerometer data were recorded from 11 PD patients, two of whom showed classical Parkinsonian-type tremor. In the OFF-state and 30 Minutes after taking their standard oral medication (ON-state), video recordings were obtained. First, we investigated the frequency and amplitude relationship between the video and accelerometer data. Then, we focused on quantifying the effect of taking standard oral treatments. Results: The data extracted from the video correlated well with the accelerometer-based measurement system. Our video-based approach identified the tremor frequency with a small error rate (mean absolute error 0.229 (±0.174) Hz) and an amplitude with a high correlation. The frequency and amplitude of the hand movement before and after medication in PD patients undergoing medication differ. PD Patients experienced a decrease in the mean value for frequency from 2.012 (±1.385) Hz to 1.526 (±1.007) Hz and in the mean value for amplitude from 8.167 (±15.687) a.u. to 4.033 (±5.671) a.u. Conclusions: Our work achieved an automatic estimation of the movement frequency, including the tremor frequency with a low error rate, and to the best of our knowledge, this is the first paper that presents automated tremor analysis before/after medication in PD, in particular using high-frame-rate video data.

Patient-led development of digital endpoints and the use of computer vision analysis in assessment of motor function in rare diseases.

Digital health technologies are transforming the way health outcomes are captured and measured. Digital biomarkers may provide more objective measurements than traditional approaches as they encompass continuous and longitudinal data collection and use of automated analysis for data interpretation. In addition, the use of digital health technology allows for home-based disease assessments, which in addition to reducing patient burden from on-site hospital visits, provides a more holistic picture of how the patient feels and functions in the real world. Tools that can robustly capture drug efficacy based on disease-specific outcomes that are meaningful to patients, are going to be key to the successful development of new treatments. This is particularly important for people living with rare and chronic complex conditions, where therapeutic options are limited and need to be developed using a patient-focused approach to achieve the biggest impact. Working in partnership with patient Organisation Duchenne UK, we co-developed a video-based approach, delivered through a new mobile health platform (DMD Home), to assess motor function in patients with Duchenne muscular dystrophy (DMD), a genetic, rare, muscular disease characterized by the progressive loss of muscle function and strength. Motor function tasks were selected to reflect the “transfer stage” of the disease, when patients are no longer able to walk independently but can stand and weight-bear to transfer. This stage is important for patients and families as it represents a significant milestone in the progression of DMD but it is not routinely captured and/or scored by standard DMD clinical and physiotherapy assessments. A total of 62 videos were submitted by eight out of eleven participants who onboarded the app and were analysed with pose estimation software (OpenPose) that led to the extraction of objective, quantitative measures, including time, pattern of movement trajectory, and smoothness and symmetry of movement. Computer vision analysis of video tasks to identify voluntary or compensatory movements within the transfer stage merits further investigation. Longitudinal studies to validate DMD home as a new methodology to predict progression to the non-ambulant stage will be pursued.

ADTIDO: Detecting the Tired Deck Officer with Fusion Feature Methods.

The incidence of maritime accidents can be significantly reduced by identifying the deck officer’s fatigue levels. The development of car driver fatigue detectors has employing electroencephalogram (EEG)-based technologies in recent years and made it possible to swiftly and accurately determine the level of a driver’s fatigue. However, individual variability and the sensitivity of EEG signals reduce the detection precision. Recently, another type of video-based technology for detecting driver fatigue by recording changes in the drivers’ eye characteristics has also been explored. In order to improve the classification performance of EEG-based approaches, this paper introduces the ADTIDO (Automatic Detect the TIred Deck Officers) algorithm, an EEG-based classification method of deck officers’ fatigue level, which combines a video-based approach to record the officer’s eye closure time for each time window. This paper uses a Discrete Wavelet Transformer (DWT) and decomposes the EEG signals into six sub-signals, from which we extract various EEG-based features, e.g., MAV, SD, and RMS. Unlike the traditional video-based method of calculating the Eyelid Closure Degree (ECD), this paper then obtains the ECD values from the EEG signals. The ECD-EEG fusion features are then created and used as the inputs for a classifier by combining the ECD and EEG feature sets. In addition, the present work develops the definition of “fatigue” at the individual level based on the real-time operational reaction time of the deck officer. To verify the efficacy of this research, the authors conducted their trials by using the EEG signals gathered from 21 subjects. It was found that Bidirectional Gated Recurrent Unit (Bi-GRU) networks outperform other classifiers, reaching a classification accuracy of 90.19 percent, 1.89 percent greater than that of only using EEG features as inputs. By combining the ADTIDO channel findings, the classification accuracy of deck officers’ fatigue levels finally reaches 95.74 percent.

Refreshing Older Adults’ Driving Skills (ROADSkills): A randomized controlled trial examining the effect of video feedback

ObjectivesThis study evaluated the effectiveness of a personalized video-based driver training program on the behind-the-wheel skills of community-dwelling older adults. MethodIn this randomized controlled trial (RCT), 80 older drivers [mean age (SD) = 71.0 (3.9)] were randomly assigned to view one of two educational videos: 1) personalized video feedback on their driving (n = 40) or 2) a generic video on aging-in-place (n = 40). The primary outcome, the total number of errors accrued in a 30-minute standardized on-road evaluation, was analyzed at baseline and 4 weeks after watching the assigned video. On-road evaluations were video-recorded and scored by a blinded rater. Self-report measures of driving abilities, behaviors, and comfort were also compared. ResultsAt the 4-week follow-up, the mean difference in the number of on-road performance errors [mean (95% CI) = -12.0 (-16.6, −7.4), p < 0.001] favored the intervention group compared to controls, with significant improvements across multiple domains: vehicle control [mean (95% CI) = -4.8 (-8.1, −1.5), p < 0.01], observing the roadway [mean (95% CI) = -5.5 (-8.4, −2.6), p < 0.001], and compliance with traffic rules [mean (95% CI) = -1.3 (-2.3, −0.2), p < 0.05]. Within-group change on behind-the-wheel errors was significant for the intervention group [mean (95% CI) = -10.3 (-13.5, −7.1), p < 0.001], but not for the control group [mean (95% CI) = 1.7 (-1.6, 4.9), p > 0.05]. No significant differences were found on the self-report measures. DiscussionA novel, video-based approach that provided personalized feedback to older drivers significantly reduced behind-the-wheel errors 4-weeks post-intervention. Changes in self-reported driving behaviors and abilities were not significant. Future research will examine the long-term effects of this approach on older adults’ actual and perceived driving abilities.

UAV Video-Based Approach to Identify Damaged Trees in Windthrow Areas

Disturbances in forest ecosystems are expected to increase by the end of the twenty-first century. An understanding of these disturbed areas is critical to defining management measures to improve forest resilience. While some studies emphasize the importance of quick salvage logging, others emphasize the importance of the deadwood for biodiversity. Unmanned aerial vehicle (UAV) remote sensing is playing an important role to acquire information in these areas through the structure-from-motion (SfM) photogrammetry process. However, the technique faces challenges due to the fundamental principle of SfM photogrammetry as a passive optical method. In this study, we investigated a UAV video-based technology called full motion video (FMV) to identify fallen and snapped trees in a windthrow area. We compared the performance of FMV and an orthomosaic, created by the SfM photogrammetry process, to manually identify fallen and snapped trees, using a ground survey as a reference. The results showed that FMV was able to identify both types of damaged trees due to the ability of video to deliver better context awareness compared to the orthomosaic, although providing lower position accuracy. In addition to its processing being simpler, FMV technology showed great potential to support the interpretation of conventional UAV remote sensing analysis and ground surveys, providing forest managers with fast and reliable information about damaged trees in windthrow areas.

Video-Based 3D pose estimation for residential roofing

ABSTRACT Residential roofers are often exposed to awkward postures and motions in a prolonged time, which may not only reduce their body stability and increase fall potential, but also increase the risk of musculoskeletal disorders (MSDs). To assess their risks of fatal and musculoskeletal injuries, it is crucial to capture 3D body poses of workers during roofing tasks. In this paper, we proposed a novel two-stage motion estimation approach based on a convolution neural network to estimate residential roofer’s body poses using three-view video data. Our approach includes two stages: (1) use of an offline multi-view model to estimate the 3D pose in a single frame; (2) use of a multi-frame model to apply temporal convolutions to refine the multi-view outputs. The performance of the approach was evaluated by comparing our estimation with the gold-standard marker-based 3D human pose during one of the common residential roofing tasks – shingle installation. The evaluation results show that the proposed multi-frame model can effectively improve the accuracy of the coordinate sequence. Moreover, these results prove that the proposed video-based motion estimation approach can efficiently and accurately locate 3D body joints and pave the way for future onsite motion analysis during roofing activities.

A video-based approach to investigating intentional teaching of mathematics in Chinese kindergartens

This video-based study investigates the extent to which Chinese teachers intentionally teach mathematics knowledge to kindergarten children. Evidence revealed that intentional teaching with the key practice of sustained shared thinking occurred more in group activities than in learning centers; however, there was insufficient intentional teaching in both learning contexts. Counting/calculation was the mostly taught mathematics knowledge in the kindergartens, followed with shapes, comparing, measurement, and space/position. These findings show how the everyday curricular practices experienced by children afford evidence of their acquisition of mathematics knowledge. This study also informs how intentional teaching can be productive in facilitating children's mathematics learning.

Digital Storytelling: A Video-based Approach for Engaging University Students in Health Education

BACKGROUND: Digital stories are short videos that use narrative structures to address health outcomes from a personal perspective. AIM: The main objective of this study was to train university students to design a video-based health education program for promoting peer-to-peer education and community awareness. METHODS: Cross-sectional study. The study was conducted as a project for medical students, enrolled in the implementation of the Egyptian Youth Initiative (EYI), executed in response to the Government of Egypt- United Nations Children’s Fund (UNICEF) Country Program of Cooperation for 2016 - 2018. A four-day train-the-trainer workshop established for capacity building on DST and video communication. RESULTS: Between June 2017 and May 2018, 120 eligible students were enrolled, among whom median age was 21 years and 67 (55%) were female. Pre- and posttests detected increased percent change of students’ knowledge regarding different health issues. Open-ended questions highlighted pros and cons of the program, the most important skills and knowledge gained, suggestions and recommendations mentioned by the students to maximize the use of this type of workshops in the framework of strengthening and activating the initiative outcome. The trainer created a grading rubric to evaluate student’s video projects. CONCLUSION: The digital story is a lay method to capture meaningful, impactful stories that can be used to advocate for public health concerns and crises. The study highlights the need for high-quality research on the impact of DST in health professionals’ education and behaviors.

Video-based quantification of human movement frequency using pose estimation: A pilot study.

Assessment of repetitive movements (e.g., finger tapping) is a hallmark of motor examinations in several neurologic populations. These assessments are traditionally performed by a human rater via visual inspection; however, advances in computer vision offer potential for remote, quantitative assessment using simple video recordings. Here, we evaluated a pose estimation approach for measurement of human movement frequency from smartphone videos. Ten healthy young participants provided videos of themselves performing five repetitive movement tasks (finger tapping, hand open/close, hand pronation/supination, toe tapping, leg agility) at four target frequencies (1–4 Hz). We assessed the ability of a workflow that incorporated OpenPose (a freely available whole-body pose estimation algorithm) to estimate movement frequencies by comparing against manual frame-by-frame (i.e., ground-truth) measurements for all tasks and target frequencies using repeated measures ANOVA, Pearson’s correlations, and intraclass correlations. Our workflow produced largely accurate estimates of movement frequencies; only the hand open/close task showed a significant difference in the frequencies estimated by pose estimation and manual measurement (while statistically significant, these differences were small in magnitude). All other tasks and frequencies showed no significant differences between pose estimation and manual measurement. Pose estimation-based detections of individual events (e.g., finger taps, hand closures) showed strong correlations (all r>0.99) with manual detections for all tasks and frequencies. In summary, our pose estimation-based workflow accurately tracked repetitive movements in healthy adults across a range of tasks and movement frequencies. Future work will test this approach as a fast, quantitative, video-based approach to assessment of repetitive movements in clinical populations.

Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects.

The human right ventricle is barely monitored during open-chest surgery due to the absence of intraoperative imaging techniques capable of elaborating its complex function. Accordingly, artificial intelligence could not be adopted for this specific task. We recently proposed a video-based approach for the real-time evaluation of the epicardial kinematics to support medical decisions. Here, we employed two supervised machine learning algorithms based on our technique to predict the patients’ outcomes before chest closure. Videos of the beating hearts were acquired before and after pulmonary valve replacement in twelve Tetralogy of Fallot patients and recordings were properly labeled as the “unhealthy” and “healthy” classes. We extracted frequency-domain-related features to train different supervised machine learning models and selected their best characteristics via 10-fold cross-validation and optimization processes. Decision surfaces were built to classify two additional patients having good and unfavorable clinical outcomes. The k-nearest neighbors and support vector machine showed the highest prediction accuracy; the patients’ class was identified with a true positive rate ≥95% and the decision surfaces correctly classified the additional patients in the “healthy” (good outcome) or “unhealthy” (unfavorable outcome) classes. We demonstrated that classifiers employed with our video-based technique may aid cardiac surgeons in decision making before chest closure.

A Bayesian Network-Based Information Fusion Combined with DNNs for Robust Video Fire Detection

Fire is an abnormal event that can cause significant damage to lives and property. Deep learning approach has made large progress in vision-based fire detection. However, there is still the problem of false detections due to the objects which have similar fire-like visual properties such as colors or textures. In the previous video-based approach, Faster Region-based Convolutional Neural Network (R-CNN) is used to detect the suspected regions of fire (SRoFs), and long short-term memory (LSTM) accumulates the local features within the bounding boxes to decide a fire in a short-term period. Then, majority voting of the short-term decisions is taken to make the decision reliable in a long-term period. To ensure that the final fire decision is more robust, however, this paper proposes to use a Bayesian network to fuse various types of information. Because there are so many types of Bayesian network according to the situations or domains where the fire detection is needed, we construct a simple Bayesian network as an example which combines environmental information (e.g., humidity) with visual information including the results of location recognition and smoke detection, and long-term video-based majority voting. Our experiments show that the Bayesian network successfully improves the fire detection accuracy when compared against the previous video-based method and the state of art performance has been achieved with a public dataset. The proposed method also reduces the latency for perfect fire decisions, as compared with the previous video-based method.

Quantitative and Qualitative Running Gait Analysis through an Innovative Video-Based Approach.

Quantitative and qualitative running gait analysis allows the early identification and the longitudinal monitoring of gait abnormalities linked to running-related injuries. A promising calibration- and marker-less video sensor-based technology (i.e., Graal), recently validated for walking gait, may also offer a time- and cost-efficient alternative to the gold-standard methods for running. This study aim was to ascertain the validity of an improved version of Graal for quantitative and qualitative analysis of running. In 33 healthy recreational runners (mean age 41 years), treadmill running at self-selected submaximal speed was simultaneously evaluated by a validated photosensor system (i.e., Optogait—the reference methodology) and by the video analysis of a posterior 30-fps video of the runner through the optimized version of Graal. Graal is video analysis software that provides a spectral analysis of the brightness over time for each pixel of the video, in order to identify its frequency contents. The two main frequencies of variation of the pixel’s brightness (i.e., F1 and F2) correspond to the two most important frequencies of gait (i.e., stride frequency and cadence). The Optogait system recorded step length, cadence, and its variability (vCAD, a traditional index of gait quality). Graal provided a direct measurement of F2 (reflecting cadence), an indirect measure of step length, and two indexes of global gait quality (harmony and synchrony index). The correspondence between quantitative indexes (Cadence vs. F2 and step length vs. Graal step length) was tested via paired t-test, correlations, and Bland–Altman plots. The relationship between qualitative indexes (vCAD vs. Harmony and Synchrony Index) was investigated by correlation analysis. Cadence and step length were, respectively, not significantly different from and highly correlated with F2 (1.41 Hz ± 0.09 Hz vs. 1.42 Hz ± 0.08 Hz, p = 0.25, r2 = 0.81) and Graal step length (104.70 cm ± 013.27 cm vs. 107.56 cm ± 13.67 cm, p = 0.55, r2 = 0.98). Bland–Altman tests confirmed a non-significant bias and small imprecision between methods for both parameters. The vCAD was 1.84% ± 0.66%, and it was significantly correlated with neither the Harmony nor the Synchrony Index (0.21 ± 0.03, p = 0.92, r2 = 0.00038; 0.21 ± 0.96, p = 0.87, r2 = 0.00122). These findings confirm the validity of the optimized version of Graal for the measurement of quantitative indexes of gait. Hence, Graal constitutes an extremely time- and cost-efficient tool suitable for quantitative analysis of running. However, its validity for qualitative running gait analysis remains inconclusive and will require further evaluation in a wider range of absolute and relative running intensities in different individuals.

Improving the Driving of Community-Dwelling Older Canadians: A Randomized Controlled Trial

Older Canadians, similar to aging drivers in many other countries, want to drive, need to drive, and live in communities where driving is both valued and necessary for out-of-home participation. Many community-dwelling seniors are medically fit-to-drive, yet their collision risk remains higher than most other age groups, which some have attributed to their propensity to drive shorter distances in high-traffic areas (Antin et al., 2017). In this randomized controlled trial, the effect of a customized video-based older driver training program on behind-the-wheel performance was captured using the latest technology for an on-road evaluation. Results indicated the mean reduction in number of driving errors [mean (95% CI)=-12.0(-16.5, -7.6),p<0.001] favoured the intervention group where their change between baseline and 4-week follow-up was statistically significant [mean(95% CI)=-10.3(-13.8, -6.8),p<0.001], but not for the control group [mean (95% CI)=1.7(-0.08, 4.2), p>0.05]. Our novel, video-based approach that provided individualized feedback improved driving performance for older drivers. Part of a symposium sponsored by Transportation and Aging Interest Group.

YouTube as a Recruitment Tool? A Reflection on Using Video to Recruit Research Participants

Abstract This article presents an innovative, video-based approach to the recruitment of research participants. A YouTube video was created and uploaded as part of a doctoral study exploring what it means to be struggling as a teacher. Following a review of the recruitment literature, which highlights a general lack of attention paid to the challenges of recruitment, the author explores the approach she took in planning the video. The video was the main promotional tool for the study and was communicated via Twitter and email. She also presents online survey findings on the perceived impact and influence of the video; the visual format, informal tone and the ability to see the researcher in person were rated very positively. A reflective analysis of the video transcript follows drawing on the literature as well as the survey findings. She concludes that video-based recruitment can be an inexpensive but powerful tool which allows a human connection with the researcher early on in the research process.

Human Interactions and Financial Investment: A Video-Based Approach

Persuasive communication functions not only through content but also delivery, e.g., facial expression, tone of voice, and diction. This paper examines the persuasiveness of delivery in start-up pitches. Using machine learning (ML) algorithms to process full pitch videos, we quantify persuasion in visual, vocal, and verbal dimensions. Positive (i.e., passionate, warm) pitches increase funding probability. Yet conditional on funding, high-positivity startups underperform. Women are more heavily judged on delivery when evaluating single-gender teams, but they are neglected when co-pitching with men in mixed-gender teams. Using an experiment, we show persuasion delivery works mainly through leading investors to form inaccurate beliefs.

A prospective trial of standard versus multimedia counseling in patients undergoing endometrial cancer surgery.

2067 Background: A patient’s understanding of surgery is often limited, especially in the setting of complex oncologic procedures. A recent review found that interventions such as the use of written materials, videos, and websites, improve patients’ knowledge of the procedure and their satisfaction with decision making. We sought to determine if a video-based approach in patients undergoing robotic endometrial cancer staging improves satisfaction with perioperative counseling. Secondary objectives were physician satisfaction, patient comprehension, and visit length. Methods: From 2018-2019, patients were randomized to standard physician education or multimedia-based education, which included watching two novel animated videos followed by focused physician counseling. Basic demographic information was collected. Patient satisfaction was assessed using the Client Satisfaction Questionnaire-8 (CSQ-8, a validated satisfaction survey, scored 8-32) and a global satisfaction score (10-point scale). Physician satisfaction was assessed using a global satisfaction score. Comprehension was assessed with a 9 question survey at 3 time points. Descriptive statistics were used to compare groups. Results: Of 76 patients randomized, the majority were Caucasian (68%), 50-70 years old (70%), and had at least some college education (75%). Most patients had undergone prior surgery (83%) and one fourth had a prior cancer diagnosis. Demographic variables and surgical history were similar between groups. The video patients reported higher satisfaction on the CSQ-8 (31.57 ± 1.02 vs 30.62 ± 2.09, p &lt; 0.05) and global satisfaction score (9.95 ± 0.23 vs 9.74 ± 0.55, p &lt; 0.05). There was no difference in comprehension scores between groups at either the initial or postoperative visit. At the time of surgery, comprehension scores were higher in the standard education group compared to the video group (p &lt; 0.01). There was no difference in physician satisfaction between groups. Among the video group, there was improvement in physician satisfaction between the first and second half of patients enrolled (p &lt; 0.05). There was no difference in visit length. Conclusions: While multimedia education improved patient satisfaction in the preoperative setting, this was not clinically significant. Provider satisfaction improved over time with the use of a video aid. Multimedia education may be implemented in perioperative counseling based on provider preference and consideration should be made for further study of satisfaction after the initial implementation period. Clinical trial information: NCT03899441.

Evaluating advanced driver-assistance system trainings using driver performance, attention allocation, and neural efficiency measures

There are about 44 million licensed older drivers in the U.S. Older adults have higher crash rates and fatalities as compared to middle-aged and young drivers, which might be associated with degradations in sensory, cognitive, and physical capabilities. Advanced driver-assistance systems (ADAS) have the potential to substantially improve safety by removing some of driver vehicle control responsibilities. However, a critical aspect of providing ADAS is educating drivers on their operational characteristics and continued use. Twenty older adults participated in a driving simulation study assessing the effectiveness of video-based and demonstration-based training protocols in learning ADAS considering gender differences. The findings revealed video-based training to be more effective than demonstration-based training in improving driver performance and reducing off-road visual attention allocation and mental workload. In addition, female drivers required lower investment of mental effort (higher neural efficiency) to maintain the performance relative to males and they were less distracted by ADAS. However, male drivers were faster in activating ADAS as compared to females since they were monitoring the status of ADAS features more frequently while driving. The findings of this study provided an empirical support for using video-based approach for learning ADAS in older adults to improve driver safety and supported previous findings on older adults’ learning that as age increases there is a tendency to prefer more passive and observational learning methods.