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INTRODUCTION: The VestAid is a tablet-based application that provides feedback about a patient's eye/head movements during exercise after concussion. The goal of this case series was to determine if VestAid could be used to detect eye-gaze accuracy in a participant exposed to directed energy (DE). MATERIALS AND METHODS: The VestAid results of a participant with DE were compared to an age- and gender-matched healthy control, a participant post-concussion, and a participant with vestibular neuritis. A tablet with VestAid software was utilized to record eye-gaze accuracy and head speed during VORx1 exercises using eye and facial recognition as participants were exposed to 12 visual scenes. RESULTS: The participant with DE consistently had difficulty with eye-gaze accuracy when the head was rotated towards the right for all trials. The participant with DE had poor eye-gaze accuracy during all phases of the head turn cycle compared to the control participant (mean 47.91%, [SD = 7.32%] for the DE participant versus mean 94.28%, [SD = 5.87%] for the control participant). Post-exercise dizziness and perceived difficulty in the 12 exercises completed by the participant with DE were strongly related (Spearman's rho = 0.7372, P = .0062). The participant with DE had the lowest scores on 10 of the 12 head movement trials. CONCLUSIONS: VestAid provided unique information about eye-gaze accuracy that detected eye movement abnormalities in the participants with DE exposure, concussion, and vestibular neuritis. The objective metrics of eye-gaze stability correlate with participants' symptoms and perceived difficulty of the eye/head movements.
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(1) Background: Current vestibular rehabilitation therapy is an exercise-based approach aimed at promoting gaze stability, habituating symptoms, and improving balance and walking in patients with mild traumatic brain injury (mTBI). A major component of these exercises is the adaptation of the vestibulo-ocular reflex (VOR) and habituation training. Due to acute injury, the gain of the VOR is usually reduced, resulting in eye movement velocity that is less than head movement velocity. There is a higher chance for the success of the therapy program if the patient (a) understands the exercise procedure, (b) performs the exercises according to the prescribed regimen, (c) reports pre- and post-exercise symptoms and perceived difficulty, and (d) gets feedback on performance. (2) Methods: The development and laboratory evaluation of VestAid, an innovative, low-cost, tablet-based system that helps patients perform vestibulo-ocular reflex (VORx1) exercises correctly at home without therapist guidance, is presented. VestAid uses the tablet camera to automatically assess patient performance and compliance with exercise parameters. The system provides physical therapists (PTs) with near real-time, objective (head speed and gaze fixation compliance), and subjective (perceived difficulty and pre- and post- exercise symptoms) metrics through a web-based provider portal. The accuracy of the head-angle and eye-gaze compliance metrics was evaluated. The accuracy of estimated head angles calculated via VestAid's low-complexity algorithms was compared to the state-of-the-art deep-learning method on a public dataset. The accuracy of VestAid's metric evaluation during the VORx1 exercises was assessed in comparison to the output of an inertial measurement unit (IMU)-based system. (3) Results: There are low mean interpeak time errors (consistently below 0.1 s) across all speeds of the VORx1 exercise, as well as consistently matching numbers of identified peaks. The spatial comparison (after adjusting for the lag measured with the cross-correlation) between the VestAid and IMU-based systems also shows good matching, as shown by the low mean absolute head angle error, in which for all speeds, the mean is less than 10 degrees. (4) Conclusions: The accuracy of the system is sufficient to provide therapists with a good assessment of patient performance. While the VestAid system's head pose evaluation model may not be perfectly accurate as a result of the occluded facial features when the head moves further towards an extreme in pitch and yaw, the head speed measurements and associated compliance measures are sufficiently accurate for monitoring patients' VORx1 exercise compliance and general performance.
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Adaptação Fisiológica , Reflexo Vestíbulo-Ocular , Exercício Físico , Movimentos da Cabeça , Humanos , TecnologiaRESUMO
OBJECTIVE: We investigated whether intelligent advanced warnings of the end of green traffic signals help drivers negotiate the dilemma zone (DZ) at signalized intersections and sought to identify behavioral mechanisms for any warning-related benefits. BACKGROUND: Prior research suggested that warnings of end of green can increase slowing and stopping frequency given the DZ, but drivers may sometimes respond to warnings by speeding up. METHOD: In two simulator studies, we compared six types of roadway or in-vehicle warnings with a no-warning control condition. Using multilevel modeling, we tested mediation models of the behavioral mechanisms underlying the effects of warnings. RESULTS: In both studies, warnings led to more stopping at DZ intersections and milder decelerations when stopping compared with no warning. Drivers' predominant response to warnings was anticipatory slowing on approaching the intersection, not speeding up. The increased stopping with warning was mediated by increased slowing. In Study I, anticipatory slowing given warnings generalized to green-light intersections where no warning was given. In Study 2, we found that lane-specific warnings (e.g., LED lights embedded in each lane) sometimes led to fewer unsafe emergency stops than did non-lane-specific roadside warnings. CONCLUSION: End-of-green warnings led to safer behavior in the DZ and on the early approach to intersections. The main mechanism for the benefits of warnings was drivers' increased anticipatory slowing on approaching an intersection. Lane-specific warnings may have some benefits over roadside warnings. APPLICATION: Applications include performance models of how drivers use end-of-green warnings, control algorithms and warning displays for intelligent intersections, and statistical methodology in human factors research.