Use of Connected Technologies to Assess Barriers and Stressors for Age and Disability-Friendly Communities.

Background: The benefits of engaging in outdoor physical activity are numerous for older adults. However, previous work on outdoor monitoring of physical activities did not sufficiently identify how older adults characterize and respond to diverse elements of urban built environments, including structural characteristics, safety attributes, and aesthetics. Objective: To synthesize emerging multidisciplinary trends on the use of connected technologies to assess environmental barriers and stressors among older adults and for persons with disability. Methods: A multidisciplinary overview and literature synthesis. Results: First, we review measurement and monitoring of outdoor physical activity in community environments and during transport using wearable sensing technologies, their contextualization and using smartphone-based applications. We describe physiological responses (e.g., gait patterns, electrodermal activity, brain activity, and heart rate), stressors and physical barriers during outdoor physical activity. Second, we review the use of visual data (e.g., Google street images, Street score) and machine learning algorithms to assess physical (e.g., walkability) and emotional stressors (e.g., stress) in community environments and their impact on human perception. Third, we synthesize the challenges and limitations of using real-time smartphone-based data on driving behavior, incompatibility with software data platforms, and the potential for such data to be confounded by environmental signals in older adults. Lastly, we summarize alternative modes of transport for older adults and for persons with disability. Conclusion: Environmental design for connected technologies, interventions to promote independence and mobility, and to reduce barriers and stressors, likely requires smart connected age and disability-friendly communities and cities.

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.

Risk factors for unintentional occupational injury among urban transit bus drivers: a cohort longitudinal study.

PURPOSE: Although many studies have focused on bus operators' occupational diseases, work-related injury and associated risk factor data are limited. The purpose of this longitudinal study was to investigate unintentional injury and exposures that may affect injury risk among metropolitan bus operators. METHODS: Demographic, work-related, and injury data obtained from a metropolitan transit company for a 5-year period, enabled estimates of rates per 100 full time equivalents (FTEs) and adjusted Hazard Ratios (HRs), with 95% confidence intervals (CIs), using Generalized Estimating Equations and Cox proportional hazards models, respectively. RESULTS: The 2095 bus operators, included in this study, had an unintentional injury rate (95% CI) of 17.8 (16.1-19.7) per 100 FTEs. Multivariable analysis identified increased risks for operators who were female, compared to male (HR = 2.4; 2.0-2.8); worked less than 7 versus 7 to less than 12 hours per day (HR = 4.6; 3.8-5.5); and drove less than 7 versus 7 to less than 12 hours per day (HR = 3.2; 2.7-3.8). Suggestive increased risks were identified for operators working split versus straight shifts (HR = 1.2; 1.0-1.4) and for driving limited versus regular bus routes (HR = 1.36; 1.0-1.8). CONCLUSIONS: Results serve as a basis for further studies and inform the development of targeted intervention strategies to reduce bus operators' occupational injuries.

A multimodal dataset for various forms of distracted driving.

We describe a multimodal dataset acquired in a controlled experiment on a driving simulator. The set includes data for n=68 volunteers that drove the same highway under four different conditions: No distraction, cognitive distraction, emotional distraction, and sensorimotor distraction. The experiment closed with a special driving session, where all subjects experienced a startle stimulus in the form of unintended acceleration-half of them under a mixed distraction, and the other half in the absence of a distraction. During the experimental drives key response variables and several explanatory variables were continuously recorded. The response variables included speed, acceleration, brake force, steering, and lane position signals, while the explanatory variables included perinasal electrodermal activity (EDA), palm EDA, heart rate, breathing rate, and facial expression signals; biographical and psychometric covariates as well as eye tracking data were also obtained. This dataset enables research into driving behaviors under neatly abstracted distracting stressors, which account for many car crashes. The set can also be used in physiological channel benchmarking and multispectral face recognition.

Can vehicle longitudinal jerk be used to identify aggressive drivers? An examination using naturalistic driving data.

This paper investigated the characteristics of vehicle longitudinal jerk (change rate of acceleration with respect to time) by using vehicle sensor data from an existing naturalistic driving study. The main objective was to examine whether vehicle jerk contains useful information that could be potentially used to identify aggressive drivers. Initial investigation showed that there are unique characteristics of vehicle jerk in drivers' gas and brake pedal operations. Thus two jerk-based metrics were examined: (1) driver's frequency of using large positive jerk when pressing the gas pedal, and (2) driver's frequency of using large negative jerk when pressing the brake pedal. To validate the performance of the two metrics, drivers were firstly divided into an aggressive group and a normal group using three classification methods (1) traveling at excessive speed (speeding), (2) following too closely to a front vehicle (tailgating), and (3) their association with crashes or near-crashes in the dataset. The results show that those aggressive drivers defined using any of the three methods above were associated with significantly higher values of the two jerk-based metrics. Between the two metrics the frequency of using large negative jerk seems to have better performance in identifying aggressive drivers. A sensitivity analysis shows the findings were largely consistent with varying parameters in the analysis. The potential applications of this work include developing quantitative surrogate safety measures to identify aggressive drivers and aggressive driving, which could be potentially used to, for example, provide real-time or post-ride performance feedback to the drivers, or warn the surrounding drivers or vehicles using the connected vehicle technologies.

The Effect of Secondary Task Engagement on Adolescents' Driving Performance and Crash Risk.

PURPOSE: The purpose of this review was to synthesize the evidence of the effects of secondary task engagement on novice adolescent's driving performance and crash risk. METHODS: Searches of multiple databases were conducted using search terms related to secondary task engagement and teenage drivers. Articles were selected for inclusion if they were: written in English, an empirical study assessing the impact of secondary task engagement on driving, and included study participants who were licensed drivers between the ages of 14 and 17 years (if research was conducted in the United States) or within 18 months licensure in other countries. Thirty-eight abstracts were reviewed. RESULTS: Fifteen studies met the inclusion criteria. Most studies examined the effects of electronic device use as the secondary task. Effects were assessed using crash databases, simulator, instrumented vehicle, and naturalistic driving studies. Texting resulted in increased lane deviations and eyes off road time in simulated driving, whereas talking on a cell phone had little effect. Naturalistic studies, which use vehicle instrumentation to measure actual driving, found secondary tasks that required drivers to look away from the forward roadway also increased the risk of crashes and near-crashes for young novice drivers, whereas tasks that did not require eyes to be off the forward roadway (e.g., talking on cell phone) had no effect on crash risk. CONCLUSIONS: Methodological differences in the definition and measurement of driving performance make it difficult to directly compare findings, even among the limited number of studies conducted. Despite this, results suggest that secondary tasks degrade driving performance and increase risk only when they require drivers to look away from the forward roadway. Future research needs to focus more explicitly on the ways in which secondary task engagement influences drivers' behavior (e.g., interfering with information acquisition or manual control of the vehicle). This, along with the use of standard measures across studies, would build a more useful body of literature on this topic.

Factors associated with civilian drivers involved in crashes with emergency vehicles.

Motor vehicle crashes involving civilian and emergency vehicles (EVs) have been a known problem that contributes to fatal and nonfatal injuries; however, characteristics associated with civilian drivers have not been examined adequately. This study used data from The National Highway Traffic Safety Administration's Fatality Analysis Reporting System and the National Automotive Sampling System General Estimates System to identify driver, roadway, environmental, and crash factors, and consequences for civilian drivers involved in fatal and nonfatal crashes with in-use and in-transport EVs. In general, drivers involved in emergency-civilian crashes (ECCs) were more often driving: straight through intersections (vs. same direction) of four-points or more (vs. not at intersection); where traffic signals were present (vs. no traffic control device); and at night (vs. midday). For nonfatal ECCs, drivers were more often driving: distracted (vs. not distracted); with vision obstructed by external objects (vs. no obstruction); on dark but lighted roads (vs. daylight); and in opposite directions (vs. same directions) of the EVs. Consequences included increased risk of injury (vs. no injury) and receiving traffic violations (vs. no violation). Fatal ECCs were associated with driving on urban roads (vs. rural), although these types of crashes were less likely to occur on dark roads (vs. daylight). The findings of this study suggest drivers may have difficulties in visually detecting EVs in different environments.

Aging and the impact of distraction on an intersection crossing assist system.

It is known that distraction reduces the benefits of collision avoidance systems by slowing a driver's response. The current study examined the impact of a drivers' use of an in-vehicle intersection crossing assist system under demanding cognitive load conditions. Forty eight drivers crossed a busy rural intersection in a simulated environment while completing four blocks of trials, in half of which they used the assist system and engaged in a working memory task. Participants were dichotomized into older and younger age groups. The results showed a tendency towards conservative driving in a single-task condition when only using the assist system. A similar shift in driving style was observed when drivers crossed the intersection while engaged in a secondary task. Using the in-vehicle intersection crossing assist system under cognitively demanding conditions did not result in adverse consequences-the impact of distraction was different compared to a typical collision avoidance system. Older drivers showed some evidence of more conservative intersection crossing, however they also appeared to rely more on the in-vehicle assist system when presented with an extraneous additional task.