Speech sounds can be classified on the basis of their underlying articulators or on the basis of the acoustic characteristics resulting from particular articulatory positions. Research in speech perception suggests that distinctive features are based on both articulatory and acoustic information. In recent years, neuroelectric and neuromagnetic investigations provided evidence for the brain's early sensitivity to distinctive feature and their acoustic consequences, particularly for place of articulation distinctions. Here, we compare English consonants in a Mismatch Field design across two broad and distinct places of articulation -LABIAL and CORONAL -and provide further evidence that early evoked auditory responses are sensitive to these features. We further add to the findings of asymmetric consonant processing, although we do not find support for coronal underspecification. Labial glides (Experiment 1) and fricatives (Experiment 2) elicited larger Mismatch responses than their coronal counterparts. Interestingly, their M100 dipoles differed along the anterior/posterior dimension in the auditory cortex that has previously been found to spatially reflect place of articulation differences. Our results are discussed with respect to acoustic and articulatory bases of featural speech sound classifications and with respect to a model that maps distinctive phonetic features onto long-term representations of speech sounds.

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Our goal is to address the need for driver-state detection using wearable and in-vehicle sensor measurements of driver physiology and health. To address this goal, we deployed in-vehicle systems, wearable sensors, and procedures capable of quantifying real-world driving behavior and performance in at-risk drivers with insulin-dependent type 1 diabetes mellitus (DM). We applied these methodologies over 4 weeks of continuous observation to quantify differences in realworld driver behavior profiles associated with physiologic changes in drivers with DM (N=19) and without DM (N=14). Results showed that DM driver behavior changed as a function of glycemic state, particularly hypoglycemia. DM drivers often drive during at-risk physiologic states, possibly due to unawareness of impairment, which in turn may relate to blunted physiologic responses (measurable heart rate) to hypoglycemia after repeated episodes of hypoglycemia. We found that this DM driver cohort has an elevated risk of crashes and citations, which our results suggest is linked to the DM driver's own momentary physiology. Overall, our findings demonstrate a clear link between at-risk driver physiology and real-world driving. By discovering key relationships between naturalistic driving and parameters of contemporaneous physiologic changes, like glucose control, this study directly advances the goal of driver-state detection through wearable physiologic sensors as well as efforts to develop "gold standard" metrics of driver safety and an individualized approach to driver health and wellness.

Objective To identify whether rheumatoid arthritis (RA) is associated with driving ability and/or the use of assistive devices or modifications to improve driving ability. Methods We conducted a systematic literature review following Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines of RA and driving ability/adaptations by searching multiple databases from inception to April 2018. Eligible studies were original articles in the English language that had quantitative data regarding the study objective and at least 5 RA patients. Similar outcomes were extracted across studies and grouped into categories for review. Results Our search yielded 1,935 potential reports, of which 22 fulfilled eligibility criteria, totaling 6,285 RA patients. The prevalence of driving issues in RA was highly variable among the studies. Some of the shared themes addressed in these publications included RA in association with rates of motor vehicle crashes, self‐reported driving difficulty, inability to drive, use of driving adaptations, use of assistance by other people for transport, and difficulty with general transportation. Conclusion Despite variability among individual reports, driving difficulties and the use of driving adaptations are relatively common in individuals with RA. Given the central importance of automobile driving for the quality of life of RA patients, further investigations of driving ability and potential driving adaptations that can help overcome barriers to safe driving are needed.

Our goal is to measure real-world effects of at-risk driver physiology on safety-critical tasks like driving by monitoring driver behavior and physiology in real-time. Drivers with type 1 diabetes (T1D) have an elevated crash risk that is linked to abnormal blood glucose, particularly hypoglycemia. We tested the hypotheses that 1) T1D drivers would have overall impaired vehicle control behavior relative to control drivers without diabetes, 2) At-risk patterns of vehicle control in T1D drivers would be linked to at-risk, in-vehicle physiology, and 3) T1D drivers would show impaired vehicle control with more recent hypoglycemia prior to driving.Methods: Drivers (18 T1D, 14 control) were monitored continuously (4-weeks) using in-vehicle sensors (e.g., video, accelerometer, speed) and wearable continuous glucose monitors (CGMs) that measured each T1D driver's real-time blood glucose. Driver vehicle control was measured by vehicle acceleration variability (AV) across lateral (AVY, steering) and longitudinal (AVX, braking/accelerating) axes in 45-second segments (N = 61,635). Average vehicle speed for each segment was modeled as a covariate of AV and mixed-effects linear regression models were used. Results:We analyzed 3,687 drives (21,231 miles). T1D drivers had significantly higher overall AVX, Y compared to control drivers (BX = 2.5×10 -2 BY = 1.6×10 -2 , p < 0.01)--which is linked to erratic steering or swerving and harsh braking/accelerating. At-risk vehicle control patterns were particularly associated with at-risk physiology, namely hypo-and hyperglycemia (higher overall AVX,Y). Impairments from hypoglycemia persisted for hours after hypoglycemia resolved, with drivers who had hypoglycemia within 2-3 hours of driving showing higher AVX and AVY.State Department of Motor Vehicle records for the 3 years preceding the study showed that at-risk T1D drivers accounted for all crashes (N = 3) and 85% of citations (N = 13) observed. Conclusions:Our results show that T1D driver risk can be linked to real-time patterns of at-risk driver physiology, particularly hypoglycemia, and driver risk can be detected during and prior to driving. Such naturalistic studies monitoring driver vehicle controls can inform methods for early detection of hypoglycemia-related driving risks, fitness to drive assessments, thereby helping to preserve safety in at-risk drivers with diabetes.

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OBJECTIVES:We test the hypothesis that clinical measures of age-related cognitive, visual, and mobility impairments negatively impact older driver speed limit compliance to advance method developments that improve older patient care and screen, identify, and advise at-risk older drivers. DESIGN: Real-world driver behaviors of older adults who had a range of cognitive, visual, and mobility abilities (measured with standardized, clinical tests) were assessed in environmental context (e.g., speed limit, traffic density, roadway type). Older driver speed limit compliance was measured in constant speed limit zones and at transition zones, where speed limits changed. SETTING: A naturalistic driving study of older adults living around Omaha, Nebraska. PARTICIPANTS: Seventy-seven, legally licensed, active, and typically aging older drivers (65-90 years) who had a range of cognitive and visual abilities. MEASUREMENTS: Drivers typical, daily driving was continuously monitored for 3 months using sensor instrumentation installed in their own vehicles. At study start, each participant completed a comprehensive, standardized, clinical assessment of cognitive, visual, and mobility abilities relevant to aging and driving. RESULTS: Older drivers with greater cognitive impairment (P = .10) drove slower than drivers with less cognitive impairment, linking cognitive impairment to speed control. Drivers with greater visual impairment overall complied less with speed limit changes at transition zones (P = .01) and were more likely to comply with speed limit transitions when they occurred concurrently with changes in roadway features (P < .01). CONCLUSION: Results link clinical measures of age-related cognitive and visual impairment to impaired driver safety in real-world contexts. Real-world sensor data coupled with detailed, personalized older driver profiles can inform patients, caregivers, interventions, policy, and the design of supportive in-vehicle technology for at-risk older drivers.