Subjective awareness of sleepiness while driving in younger and older adults

Understanding whether drivers can accurately assess sleepiness is essential for educational campaigns advising drivers to stop driving when feeling sleepy. However, few studies have examined this in real-world driving environments, particularly among older drivers who comprise a large proportion of all road users. To examine the accuracy of subjective sleepiness ratings in predicting subsequent driving impairment and physiological drowsiness, 16 younger (21-33 years) and 17 older (50-65 years) adults drove an instrumented vehicle for 2 h on closed loop under two conditions: well-rested and 29 h sleep deprivation. Sleepiness ratings (Karolinska Sleepiness Scale, Likelihood of Falling Asleep scale, Sleepiness Symptoms Questionnaire) were obtained every 15min, alongside lane deviations, near crash events, and ocular indices of drowsiness. All subjective sleepiness measures increased with sleep deprivation for both age groups (p < 0.013). While most subjective sleepiness ratings significantly predicted driving impairment and drowsiness in younger adults (OR: 1.7-15.6, p < 0.02), this was only apparent for KSS, likelihood of falling asleep, and "difficulty staying in the lane for the older adults" (OR: 2.76-2.86, p = 0.02). This may be due to an altered perception of sleepiness in older adults, or due to lowered objective signs of impairment in the older group. Our data suggest that (i) younger and older drivers are aware of sleepiness; (ii) the best subjective scale may differ across age groups; and (iii) future research should expand on the best subjective measures to inform of crash risk in older adults to inform tailored educational road safety campaigns on signs of sleepiness.

Get Ready for Take-Overs: Using Head-Up Display for Drivers to Engage in Non-Driving-Related Tasks in Automated Vehicles

OBJECTIVE

The study aims to investigate the potential of using HUD (head-up display) as an approach for drivers to engage in non-driving-related tasks (NDRTs) during automated driving, and examine the impacts on driver state and take-over performance in comparison to the traditional mobile phone.

BACKGROUND

Advances in automated vehicle technology have the potential to relieve drivers from driving tasks so that they can engage in NDRTs freely. However, drivers will still need to take-over control under certain circumstances.

METHOD

A driving simulation experiment was conducted using an Advanced Driving Simulator and real-world driving videos. Forty-six participants completed three drives in three display conditions, respectively (HUD, mobile phone and baseline without NDRT). The HUD was integrated with the vehicle in displaying NDRTs while the mobile phone was not. Drivers' visual (e.g. gaze, blink) and physiological (e.g. ECG, EDA) data were collected to measure driver state. Two take-over reaction times (hand and foot) were used to measure take-over performance.

RESULTS

The HUD significantly shortened the take-over reaction times compared to the mobile phone condition. Compared to the baseline condition, drivers in the HUD condition also experienced lower cognitive workload and physiological arousal. Drivers' take-over reaction times were significantly correlated with their visual and electrodermal activities during automated driving prior to the take-over request.

CONCLUSION

HUDs can improve driver performance and lower workload when used as an NDRT interface.

APPLICATION

The study sheds light on a promising approach for drivers to engage in NDRTs in future AVs.

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

OBJECTIVE

examine the prevalence of driver distraction in naturalistic driving when implementing European New Car Assessment Program (Euro NCAP)-defined distraction behaviours.

BACKGROUND

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.

METHOD

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.

RESULTS

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.

CONCLUSION

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.

APPLICATION

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.

Evaluating Driver Features for Cognitive Distraction Detection and Validation in Manual and Level 2 Automated Driving

OBJECTIVE

This study aimed to investigate the impacts of feature selection on driver cognitive distraction (CD) detection and validation in real-world nonautomated and Level 2 automated driving scenarios.

BACKGROUND

Real-time driver state monitoring is critical to promote road user safety.

METHOD

Twenty-four participants were recruited to drive a Tesla Model S in manual and Autopilot modes on the highway while engaging in the N-back task. In each driving mode, CD was classified by the random forest algorithm built on three "hand-crafted" glance features (i.e., percent road center [PRC], the standard deviation of gaze pitch, and yaw angles), or through a large number of features that were transformed from the output of a driver monitoring system (DMS) and other sensing systems.

RESULTS

In manual driving, the small set of glance features was as effective as the large set of machine-generated features in terms of classification accuracy. Whereas in Level 2 automated driving, both glance and vehicle features were less sensitive to CD. The glance features also revealed that the misclassified driver state was the result of the dynamic fluctuations and individual differences of cognitive loads under CD.

CONCLUSION

Glance metrics are critical for the detection and validation of CD in on-road driving.

APPLICATIONS

The paper suggests the practical value of human factors domain knowledge in feature selection and ground truth validation for the development of driver monitoring technologies.

Beyond gaze fixation: Modeling peripheral vision in relation to speed, Tesla Autopilot, cognitive load, and age in highway driving

OBJECTIVE

The study aims to model driver perception across the visual field in dynamic, real-world highway driving.

BACKGROUND

Peripheral vision acquires information across the visual field and guides a driver's information search. Studies in naturalistic settings are lacking however, with most research having been conducted in controlled simulation environments with limited eccentricities and driving dynamics.

METHODS

We analyzed data from 24 participants who drove a Tesla Model S with Autopilot on the highway. While driving, participants completed the peripheral detection task (PDT) using LEDs and the N-back task to generate cognitive load. The I-DT (identification by dispersion threshold) algorithm sampled naturalistic gaze fixations during PDTs to cover a broader and continuous spectrum of eccentricity. A generalized Bayesian regression model predicted LED detection probability during the PDT-as a surrogate for peripheral vision-in relation to eccentricity, vehicle speed, driving mode, cognitive load, and age.

RESULTS

The model predicted that LED detection probability was high and stable through near-peripheral vision but it declined rapidly beyond 20°-30° eccentricity, showing a narrower useful field over a broader visual field (maximum 70°) during highway driving. Reduced speed (while following another vehicle), cognitive load, and older age were the main factors that degraded the mid-peripheral vision (20°-50°), while using Autopilot had little effect.

CONCLUSIONS

Drivers can reliably detect objects through near-peripheral vision, but their peripheral detection degrades gradually due to further eccentricity, foveal demand during low-speed vehicle following, cognitive load, and age.

APPLICATIONS

The findings encourage the development of further multivariate computational models to estimate peripheral vision and assess driver situation awareness for crash prevention.

Effects of Distraction in On-Road Level 2 Automated Driving: Impacts on Glance Behavior and Takeover Performance

OBJECTIVE

The paper aimed to investigate glance behaviors under different levels of distraction in automated driving (AD) and understand the impact of distraction levels on driver takeover performance.

BACKGROUND

Driver distraction detrimentally affects takeover performance. Glance-based distraction measurement could be a promising method to remind drivers to maintain enough attentiveness before the takeover request in partially AD.

METHOD

Thirty-six participants were recruited to drive a Tesla Model S in manual and Autopilot modes on a test track while engaging in secondary tasks, including temperature-control, email-sorting, and music-selection, to impose low and high distractions. During the test drive, participants needed to quickly change the lane as if avoiding an immediate road hazard if they heard an unexpected takeover request (an auditory warning). Driver state and behavior over the test drive were recorded in real time by a driver monitoring system and several other sensors installed in the Tesla vehicle.

RESULTS

The distribution of off-road glance duration was heavily skewed (with a long tail) by high distractions, with extreme glance duration more than 30 s. Moreover, being eyes-off-road before takeover could cause more delay in the urgent takeover reaction compared to being hands-off-wheel.

CONCLUSION

The study measured off-road glance duration under different levels of distraction and demonstrated the impacts of being eyes-off-road and hands-off-wheel on the following takeover performance.

APPLICATION

The findings provide new insights about engagement in Level 2 AD and are useful for the design of driver monitoring technologies for distraction management.

The impact of 7-hour and 11-hour rest breaks between shifts on heavy vehicle truck drivers' sleep, alertness and naturalistic driving performance

BACKGROUND

An inadequate rest break between shifts may contribute to driver sleepiness. This study assessed whether extending the major rest break between shifts from 7-hours (Australian industry standard) to 11-hours, improved drivers' sleep, alertness and naturalistic driving performance.

METHODS

17 heavy vehicle drivers (16 male) were recruited to complete two conditions. Each condition comprised two 13-hour shifts, separated by either a 7- or 11-hour rest break. The initial 13-hour shift was the drivers' regular work. The rest break and following 13-hour shift were simulated. The simulated shift included 5-hours of naturalistic driving with measures of subjective sleepiness, physiological alertness (ocular and electroencephalogram) and performance (steering and lane departures).

RESULTS

13 drivers provided useable data. Total sleep during the rest break was greater in the 11-hour than the 7-hour condition (median hours [25^th to 75^th percentile] 6.59 [6.23, 7.23] vs. 5.07 [4.46, 5.38], p = 0.008). During the simulated shift subjective sleepiness was marginally better for the 11-hour condition (mean Karolinska Sleepiness Scale [95^th CI] = 4.52 [3.98, 5.07] vs. 5.12 [4.56, 5.68], p = 0.009). During the drive, ocular and vehicle metrics were improved for the 11-hour condition (p<0.05). Contrary to expectations, mean lane departures p/hour were increased during the 11-hour condition (1.34 [-0.38,3.07] vs. 0.63 [-0.2,1.47], p = 0.027).

CONCLUSIONS

Extending the major rest between shifts substantially increases sleep duration and has a modest positive impact on driver alertness and performance. Future work should replicate the study in a larger sample size to improve generalisability and assess the impact of consecutive 7-hour major rest breaks.

The Impacts of Temporal Variation and Individual Differences in Driver Cognitive Workload on ECG-Based Detection

OBJECTIVE

This paper aimed to investigate the robustness of driver cognitive workload detection based on electrocardiogram (ECG) when considering temporal variation and individual differences in cognitive workload.

BACKGROUND

Cognitive workload is a critical component to be monitored for error prevention in human-machine systems. It may fluctuate instantaneously over time even in the same tasks and differ across individuals.

METHOD

A driving simulation study was conducted to classify driver cognitive workload underlying four experimental conditions (baseline, N-back, texting, and N-back + texting distraction) in two repeated 1-hr blocks. Heart rate (HR) and heart rate variability (HRV) were compared among the experimental conditions and between the blocks. Random forests were built on HR and HRV to classify cognitive workload in different blocks and for different individuals.

RESULTS

HR and HRV were significantly different between repeated blocks in the study, demonstrating the time-induced variation in cognitive workload. The performance of cognitive workload classification across blocks and across individuals was significantly improved after normalizing HR and HRV in each block by the corresponding baseline.

CONCLUSION

The temporal variation and individual differences in cognitive workload affects ECG-based cognitive workload detection. But normalization approaches relying on the choice of appropriate baselines help compensate for the effects of temporal variation and individual differences.

APPLICATION

The findings provide insight into the value and limitations of ECG-based driver cognitive workload monitoring during prolonged driving for individual drivers.

Cognition and cerebrovascular function in primary hyperparathyroidism before and after parathyroidectomy

PURPOSE

There are cognitive changes in primary hyperparathyroidism (PHPT) that improve with parathyroidectomy, but the mechanism of cognitive dysfunction has not been delineated. We assessed if cerebrovascular function is impaired in PHPT, improves post-parathyroidectomy and is associated with PTH level and cognitive dysfunction.

METHODS

This is an observational study of 43 patients with mild hypercalcemic or normocalcemic PHPT or goiter. At baseline, cerebrovascular function (dynamic cerebral autoregulation and vasomotor reactivity) by transcranial Doppler and neuropsychological function were compared between all three groups. A subset underwent parathyroidectomy or thyroidectomy, and was compared 6 months post-operatively.

RESULTS

Mean cerebrovascular and neuropsychological function was normal and no worse in PHPT compared to controls preoperatively. Higher PTH was associated with worse intracerebral autoregulation (r = - 0.43, p = 0.02) and worse cognitive performance on some tests. Post-parathyroidectomy, mood improved significantly, but changes did not differ compared to those having thyroidectomy (p = 0.84). There was no consistent improvement in cognition or change in vascular function in either surgical group.

CONCLUSIONS

Although higher PTH was associated with worse intracerebral autoregulation, cerebrovascular function, cognition and mood were normal in mild PHPT. PTX did not improve vascular or cognitive function. The observed improvement in mood cannot be clearly attributed to PTX. Notwithstanding the small sample size, the results do not support changing current criteria for parathyroidectomy to include cognitive complaints. However, the associations between PTH, cognition and cerebral autoregulation merit future studies in those with more severe hyperparathyroidism.

A pre-drive ocular assessment predicts alertness and driving impairment: A naturalistic driving study in shift workers

Sleepiness is a major contributor to motor vehicle crashes and shift workers are particularly vulnerable. There is currently no validated objective field-based measure of sleep-related impairment prior to driving. Ocular parameters are promising markers of continuous driver alertness in laboratory and track studies, however their ability to determine fitness-to-drive in naturalistic driving is unknown. This study assessed the efficacy of a pre-drive ocular assessment for predicting sleep-related impairment in naturalistic driving, in rotating shift workers. Fifteen healthcare workers drove an instrumented vehicle for 2 weeks, while working a combination of day, evening and night shifts. The vehicle monitored lane departures and behavioural microsleeps (blinks >500 ms) during the drive. Immediately prior to driving, ocular parameters were assessed with a 4-min test. Lane departures and behavioural microsleeps occurred on 17.5 % and 10 % of drives that had pre-drive assessments, respectively. Pre-drive blink duration significantly predicted behavioural microsleeps and showed promise for predicting lane departures (AUC = 0.79 and 0.74). Pre-drive percentage of time with eyes closed had high accuracy for predicting lane departures and behavioural microsleeps (AUC = 0.73 and 0.96), although was not statistically significant. Pre-drive psychomotor vigilance task variables were not statistically significant predictors of lane departures. Self-reported sleep-related and hazardous driving events were significantly predicted by mean blink duration (AUC = 0.65 and 0.69). Measurement of ocular parameters pre-drive predict drowsy driving during naturalistic driving, demonstrating potential for fitness-to-drive assessment in operational environments.

Combined cancer patient-reported symptom and health utility tool for routine clinical implementation: a real-world comparison of the ESAS and EQ-5D in multiple cancer sites

Background

We assessed whether the presence and severity of common cancer symptoms are associated with the health utility score (hus) generated from the EQ-5D (EuroQol Research Foundation, Rotterdam, Netherlands) in patients with cancer and evaluated whether it is possible pragmatically to integrate routine hus and symptom evaluation in our cancer population.

Methods

Adult outpatients at Princess Margaret Cancer Centre with any cancer were surveyed cross-sectionally using the Edmonton Symptom Assessment System (esas) and the EQ-5D-3L, and results were compared using Spearman correlation coefficients and regression analyses.

Results

Of 764 patients analyzed, 27% had incurable disease. We observed mild-to-moderate correlations between each esas symptom score and the hus (Spearman coefficients: -0.204 to -0.416; p < 0.0001 for each comparison), with the strongest associations being those for pain (R = -0.416), tiredness (R = -0.387), and depression (R =-0.354). Multivariable analyses identified pain and depression as highly associated (both p < 0.0001) and tiredness as associated (p = 0.03) with the hus. The ability of the esas to predict the hus was low, at 0.25. However, by mapping esas pain, anxiety, and depression scores to the corresponding EQ-5D questions, we could derive the hus using partial esas data, with Spearman correlations of 0.83-0.91 in comparisons with direct EQ-5D measurement of the hus.

Conclusions

The hus derived from the EQ-5D-3L is associated with all major cancer symptoms as captured by the esas. The esas scores alone could not predict EQ-5D scores with high accuracy. However, esas-derived questions assessing the same domains as the EQ-5D-3L questions could be mapped to their corresponding EQ-5D questions to generate the hus, with high correlation to the directly measured hus. That finding suggests a potential approach to integrating routine symptom and hus evaluations after confirmatory studies.

Patterns of Sequential Off-Road Glances Indicate Levels of Distraction in Automated Driving

The safety concerns linked to semi-automated driving – more automation, less driver engagement – could be resolved by real-time driver monitoring with mitigation strategies. To achieve this, this paper analyzed an on-road dataset of sequential off-road glance behaviors under different levels of distraction in an autonomous vehicle trial named CANdrive. Several metrics based on sequential off-road glances were proposed and examined in terms of their capacity of measuring the levels of distraction. These findings are useful for the development of high-resolution driver state monitoring to improve safety in the collaboration between human driver and semi-autonomous vehicle.

Analysis of Gaze Behavior to Measure Cognitive Distraction in Real-World Driving

Cognitive distraction can impair drivers’ situation awareness and control performance in driving. An on-road study was conducted to examine the efficacy in the detection of driver cognitive distraction based on the driver monitoring system developed by Seeing Machines. Participants completed a 25-km test drive on the local public roads whilst engaging in a series of secondary tasks that were designed to trigger different types of cognitive distraction, such as conversation, comprehension, N-back, and route-planning tasks. The findings showed that percent road center (PRC), one of the promising gaze metrics, increased significantly with cognitive distraction when compared to baseline, but failed to distinguish between different forms of cognitive distraction Moreover, PRC’s sensitivity to cognitive distraction was found to be affected by the chosen radius of road center area. These findings of driver cognitive distraction measurement provide data-driven suggestions for the development of real-time driver monitoring systems in the wild.

Automated recognition of rear seat occupants' head position using Kinect™ 3D point cloud

INTRODUCTION

Child occupant safety in motor-vehicle crashes is evaluated using Anthropomorphic Test Devices (ATD) seated in optimal positions. However, child occupants often assume suboptimal positions during real-world driving trips. Head impact to the seat back has been identified as one important injury causation scenario for seat belt restrained, head-injured children (Bohman et al., 2011). There is therefore a need to understand the interaction of children with the Child Restraint System to optimize protection.

METHOD

Naturalistic driving studies (NDS) will improve understanding of out-of-position (OOP) trends. To quantify OOP positions, an NDS was conducted. Families used a study vehicle for two weeks during their everyday driving trips. The positions of rear-seated child occupants, representing 22 families, were evaluated. The study vehicle - instrumented with data acquisition systems, including Microsoft Kinect™ V1 - recorded rear seat occupants in 1120 driving 26 trips. Three novel analytical methods were used to analyze data. To assess skeletal tracking accuracy, analysts recorded occurrences where Kinect™ exhibited invalid head recognition among a randomly-selected subset (81 trips). Errors included incorrect target detection (e.g., vehicle headrest) or environmental interference (e.g., sunlight). When head data was present, Kinect™ was correct 41% of the time; two other algorithms - filtering for extreme motion, and background subtraction/head-based depth detection are described in this paper and preliminary results are presented. Accuracy estimates were not possible because of their experimental nature and the difficulty to use a ground truth for this large database. This NDS tested methods to quantify the frequency and magnitude of head positions for rear-seated child occupants utilizing Kinect™ motion-tracking.

RESULTS

This study's results informed recent ATD sled tests that replicated observed positions (most common and most extreme), and assessed the validity of child occupant protection on these typical CRS uses.

SUMMARY

Optimal protection in vehicles requires an understanding of how child occupants use the rear seat space. This study explored the feasibility of using Kinect™ to log positions of rear seated child occupants. Initial analysis used the Kinect™ system's skeleton recognition and two novel analytical algorithms to log head location.

PRACTICAL APPLICATIONS

This research will lead to further analysis leveraging Kinect™ raw data - and other NDS data - to quantify the frequency/magnitude of OOP situations, ATD sled tests that replicate observed positions, and advances in the design and testing of child occupant protection technology.

Naturalistic driving study of rear seat child occupants: Quantification of head position using a Kinect™ sensor

OBJECTIVE

Restraint performance is evaluated using anthropomorphic test devices (ATDs) positioned in prescribed, optimal seating positions. Anecdotally, humans-children in particular-assume a variety of positions that may affect restraint performance. Naturalistic driving studies (NDSs), where cameras and other data acquisition systems are placed in a vehicle used by participants during their regular transportation, offer means to collect these data. To date, these studies have used conventional video and analysis methods and, thus, analyses have largely been qualitative. This article describes a recently completed NDS of child occupants in which their position was monitored using a Kinect sensor to quantify their head position throughout normal, everyday driving trips.

METHODS

A study vehicle was instrumented with a data acquisition system to measure vehicle dynamics, a set of video cameras, and a Kinect sensor providing 3D motion capture at 1 Hz of the rear seat occupants. Participant families used the vehicle for all driving trips over 2 weeks. The child occupants' head position was manually identified via custom software from each Kinect color image. The 3D head position was then extracted and its distribution summarized by seat position (left, rear, center) and restraint type (forward-facing child restraint system [FFCRS], booster seat, seat belt).

RESULTS

Data from 18 families (37 child occupants) resulted in 582 trips (with children) for analysis. The average age of the child occupants was 45.6 months and 51% were male. Twenty-five child occupants were restrained in FFCRS, 9 in booster seats, and 3 in seat belts. As restraint type moved from more to less restraint (FFCRS to booster seat to seat belt), the range of fore-aft head position increased: 218, 244, and 340 mm on average, respectively. This observation was also true for left-right movement for every seat position. In general, those in the center seat position demonstrated a smaller range of head positions.

CONCLUSIONS

For the first time in a naturalistic setting, the range of head positions for child occupants was quantified. More variability was observed for those restrained in booster seats and seat belts than for those in FFCRS. The role of activities, in particular interactions with electronic devices, on head position was notable; this will be the subject of further analysis in other components of the broader study. These data can lead to solutions for optimal protection for occupants who assume positions that differ from prescribed, optimal testing positions.

Modeling Driving Performance Using In-Vehicle Speech Data From a Naturalistic Driving Study

OBJECTIVE

We aimed to (a) describe the development and application of an automated approach for processing in-vehicle speech data from a naturalistic driving study (NDS), (b) examine the influence of child passenger presence on driving performance, and (c) model this relationship using in-vehicle speech data.

BACKGROUND

Parent drivers frequently engage in child-related secondary behaviors, but the impact on driving performance is unknown. Applying automated speech-processing techniques to NDS audio data would facilitate the analysis of in-vehicle driver-child interactions and their influence on driving performance.

METHOD

Speech activity detection and speaker diarization algorithms were applied to audio data from a Melbourne-based NDS involving 42 families. Multilevel models were developed to evaluate the effect of speech activity and the presence of child passengers on driving performance.

RESULTS

Speech activity was significantly associated with velocity and steering angle variability. Child passenger presence alone was not associated with changes in driving performance. However, speech activity in the presence of two child passengers was associated with the most variability in driving performance.

CONCLUSION

The effects of in-vehicle speech on driving performance in the presence of child passengers appear to be heterogeneous, and multiple factors may need to be considered in evaluating their impact. This goal can potentially be achieved within large-scale NDS through the automated processing of observational data, including speech.

APPLICATION

Speech-processing algorithms enable new perspectives on driving performance to be gained from existing NDS data, and variables that were once labor-intensive to process can be readily utilized in future research.

Viscoelastic shear properties of porcine temporomandibular joint disc

OBJECTIVES

To investigate the intrinsic viscoelastic shear properties in porcine TMJ discs.

MATERIALS AND METHODS

Twelve fresh porcine TMJ discs from young adult pigs (6-8 months) were used. Cylindrical samples (5 mm diameter) with uniform thickness (~1.2 mm) were prepared from five regions of the TMJ disc. Torsional shear tests were performed under 10% compressive strain. Dynamic shear was applied in two methods: 1) a frequency sweep test over the frequency range of 0.1-10 rad/s with a constant shear strain amplitude of 0.05 rad and 2) a strain sweep test over the range of 0.005-0.15 rad at a constant frequency of 10 rad/s. Transient stress relaxation tests were also performed to determine the equilibrium shear properties.

RESULTS

As the frequency increased in the frequency sweep test, the dynamic shear complex modulus increased, with values ranging from 7 to 17 kPa. The phase angle, ranging from 11 to 15 degrees, displayed no pattern of regional variation as the frequency increased. The dynamic shear modulus decreased as the shear strain increased. The equilibrium shear modulus had values ranging from 2.6 to 4 kPa. The posterior region had significantly higher values for dynamic shear modulus than those in the anterior region, while no significant regional difference was found for equilibrium shear modulus.

CONCLUSION

Our results suggest that the intrinsic region-dependent viscoelastic shear characteristics of TMJ disc may play a crucial role in determining the local strain of the TMJ disc under mechanical loading.

Evaluation of a video-based measure of driver heart rate

INTRODUCTION

Internal driver events such as emotional arousal do not consistently elicit observable behaviors. However, heart rate (HR) offers promise as a surrogate measure for predicting these states in drivers. Imaging photoplethysmography (IPPG) can measure HR from face video recorded in static, indoor settings, but has yet to be examined in an in-vehicle driving environment.

METHODS

Participants (N=10) completed an on-road driving task whilst wearing a commercial, chest-strap style heart rate monitor ("baseline"). IPPG was applied to driver face video to estimate HR and the two measures of HR were compared.

RESULTS

For 4 of 10 participants, IPPG produced a valid HR signal (±5 BPM of baseline) between 48 and 75% of trip duration. For the remaining participants, IPPG accuracy was poor (<20%).

CONCLUSIONS

In-vehicle IPPG is achievable, but significant challenges remain.

PRACTICAL APPLICATIONS

The relationship between IPPG accuracy and various confounding factors was quantified for future refinement.

Examining physiological responses across different driving maneuvers during an on-road driving task: a pilot study comparing older and younger drivers

OBJECTIVE

This pilot study aimed to investigate physiological responses during an on-road driving task for older and younger drivers.

METHODS

Five older drivers (mean age = 74.60 years [2.97]) and 5 younger drivers (mean age = 30.00 years [3.08]) completed a series of cognitive assessments (Montreal Cognitive Assessment [MoCA], Mini Mental Status Examination [MMSE]; Trail Making Test [Trails A and Trails B]) and an on-road driving task along a predetermined, standardized urban route in their own vehicle. Driving performance was observed and scored by a single trained observer using a standardized procedure, where driving behaviors (appropriate and inappropriate) were scored for intersection negotiation, lane changing, and merging. During the on-road driving task, participants' heart rate (HR) was monitored with an unobtrusive physiological monitor.

RESULTS

Younger drivers performed significantly better on all cognitive assessments compared to older drivers (MoCA: t(8) = 3.882, P <.01; MMSE: t(8) = 2.954, P <.05; Trails A: t(8) = -2.499, P <.05; Trails B: t(8) = -3.262, P <.05). Analyses of participants' performance during the on-road driving task revealed a high level of appropriate overall driving behavior (M = 87%, SD = 7.62, range = 73-95%), including intersection negotiation (M = 89%, SD = 8.37%), lane changing (M = 100%), and merging (M = 53%, SD = 28.28%). The overall proportion of appropriate driving behavior did not significantly differ across age groups (younger drivers: M = 87.6%, SD = 9.04; older drivers: M = 87.0%, SD = 6.96; t(8) = 0.118, P =.91).

CONCLUSIONS

Although older drivers scored lower than younger drivers on the cognitive assessments, there was no indication of cognitive overload among older drivers based on HR response to the on-road driving task. The results provide preliminary evidence that mild age-related cognitive impairment may not pose a motor vehicle crash hazard for the wider older driver population. To maintain safe mobility of the aging population, further research into the specific crash risk factors in the older driver population is warranted.

Computer vision and driver distraction: developing a behaviour-flagging protocol for naturalistic driving data

Naturalistic driving studies (NDS) allow researchers to discreetly observe everyday, real-world driving to better understand the risk factors that contribute to hazardous situations. In particular, NDS designs provide high ecological validity in the study of driver distraction. With increasing dataset sizes, current best practice of manually reviewing videos to classify the occurrence of driving behaviours, including those that are indicative of distraction, is becoming increasingly impractical. Current statistical solutions underutilise available data and create further epistemic problems. Similarly, technical solutions such as eye-tracking often require dedicated hardware that is not readily accessible or feasible to use. A computer vision solution based on open-source software was developed and tested to improve the accuracy and speed of processing NDS video data for the purpose of quantifying the occurrence of driver distraction. Using classifier cascades, manually-reviewed video data from a previously published NDS was reanalysed and used as a benchmark of current best practice for performance comparison. Two software coding systems were developed - one based on hierarchical clustering (HC), and one based on gender differences (MF). Compared to manual video coding, HC achieved 86 percent concordance, 55 percent reduction in processing time, and classified an additional 69 percent of target behaviour not previously identified through manual review. MF achieved 67 percent concordance, a 75 percent reduction in processing time, and classified an additional 35 percent of target behaviour not identified through manual review. The findings highlight the improvements in processing speed and correctly classifying target behaviours achievable through the use of custom developed computer vision solutions. Suggestions for improved system performance and wider implementation are discussed.

SU-E-J-108: Quantitative Analysis of Longitudinal Cognitive Impairment Due to Radiation Therapy Based on Automatic Segmentation of Hippocampus and Subcortical Structure

PURPOSE

In this study, we developed a quantitative analysis tool based on patient's longitudinal MR images to 1) measure the radiation dose received by each subcortical structure, 2) follow the change of volume and shape of each structure longitudinally. This tool provides a systematic approach to study the radiation therapy (and subsequent chemotherapy) associated with cognitive impairments.

METHODS

MRI scans of one patient taken before and after radiation therapy are demonstrated in this study. 3D Conformal radiation therapy was performed on RapidArc™. An open source MRI analysis tool, FMRIB's Integrated Registration and Segmentation Tool (FIRST), was used for segmentation. The images are registered to a standard template with expert-defined labeling for all sub-cortical structures, and the labeling of each structure is mapped back to the individual MRI space for segmentation. After the segmentation, the radiation dose map was coregistered to the MRI space to calculate the dose received by each structure.

RESULTS

For the structure that is contained within the radiation zone, we can calculate the total dose based on the volumetric distribution of radiation dose. For the structure that is outside the radiation field, we can calculate the distance from the radiation zone. We have demonstrated in this work that the analysis can be done for all segmented sub-cortical structures. The change of volume before and after radiation treatment can be analyzed, and the results can be correlated with the change of cognitive performance over time.

CONCLUSIONS

We presented an automated tool for efficient, quantitative and user-independent measurements of radiation dose in subcortical structures. The obtained results can be correlated with the cognitive test score and the clinical outcome to evaluate radiation and the subsequent chemotherapy induced changes in brain structures and functions.