Motor vehicle accidents account for 47% of accidental deaths1 and 94% of all transportation-related fatalities in the United States,2 and are the leading cause of death in the 1- to 33-year age group.1 The distribution of fatalities by age is bimodal, with both young (age range, 15–24 years) and older (75+ years) groups being most affected, and consequently much research has focused on the effects of the inexperience of novice drivers3–5; and on the sensorimotor, cognitive, and visual deficits associated with aging (see Wood6 for an extensive review). Driving is a vision-intensive task, and recent studies have demonstrated that age-related decrements in vision increase the probability of an accident, as determined from a statistical analysis of visual ability and driving records,7–9 and functional studies of road sign and hazard recognition on a closed-road circuit.10 Visual pathology is unlikely to be a major contributing factor to the increased fatality rate for the younger (age range, 15–24 years) group of drivers, but recent open-road studies have identified a more limited pattern of gaze fixations in novice drivers as compared with those with more experience.3, 4 Thus, both visual function and the scanning techniques learned by drivers are important aspects of controlling an automobile.
Gaze analysis is a useful tool in understanding the visual behaviors underlying driving such as fixation of the tangent point when negotiating a curve,11, 12 the near (9-m) and far (16-m) points of regard for maintaining position in lane and assessment of road curvature,12 the wider distribution of horizontal gaze movements in drivers of large vehicles at intersections,13 and differences in the scanning patterns of novice and experienced drivers.3, 4 All these studies have used some combination of eye-in-head tracking and head-referenced scene images to analyze where the driver was looking; including manual analysis of videotaped images of the eye11, 12 and a commercial eye tracker (Eyemark VII, NAC Inc., Tokyo, Japan) measuring reflection of an infrared (IR) light source off the sclera (IR oculography [IROG]).3, 4 However, these approaches have limitations. Manual processing is clearly a time-consuming task,a and the IROG technique is inaccurate, prone to thermal drift, and essentially restricted to horizontal eye movements,14 allowing only a general description of where a driver is looking.
Clinical assessment of eye position has commonly used electro-oculography (EOG), in which surface electrodes measure variations in the electrical potential of the eye, and this technique has been used in flight simulator studies of pilot performance.15 EOG, however, has similar drawbacks to the IROG technique.14 Advances in image processing hardware has led to the development of automated noninvasive techniques for measurement of eye position using images from video cameras (video oculography [VOG]).16–27 Current commercial VOG systems use analog video cameras, requiring desktop computers with video acquisition cards to process eye movements, limiting their use in a real-world setting (e.g., Vision 2000, El Mar, Toronto, Canada; iView X, SMI, Teltow, Germany; ISCAN, Cambridge, MA). Moreover, the head-mounted cameras add significant mass to the head (300-g Vision 2000; 450-g iView X), which may impact operator behavior. Commercial VOG systems incorporating a scene camera to study gaze patterns have recently been used in flight simulator studies28 and for monitoring the gaze of pedestrians crossing an intersection29 (recording images to VCRs in the subject’s backpack) and walking through a complex indoor environment30 (with a 30-m umbilical cable).
An often neglected aspect of viewing behavior while driving is movement of the head. Two on-road studies have investigated one-dimensional head movement: head yaw (estimated from the position of a radio antenna in the scene image) in racing car drivers showed that subjects “steered” with their heads by pointing the naso-occipital axis into the turn,31 much like humans walking around a corner,32 and the magnitude of roll (lateral) tilt of the head into a turn (measured with an accelerometer) was related to road curvature.33
Head movement is particularly relevant to the study of vestibular-related driving difficulties. The peripheral vestibular labyrinths sense angular and linear head movement, and this information is centrally processed to provide postural stability, compensatory eye movements (the vestibulo-ocular reflex), and awareness of body position in space (spatial orientation). Damage to the vestibular system may occur after head injury,34 viral infection of the vestibular nerve,35 minor strokes involving the anterior inferior cerebellar artery,36 use of ototoxic antibiotics (such as gentamicin),37 and surgical procedures. Age-related decrements in vestibular function are also well-documented,38, 39 likely related to degeneration at both the peripheral40–42 and central43 level. Vestibulopathy is typically manifested as vertigo (sensation of spinning) and disturbed vision (inappropriate nystagmus, oscillopsia), often provoked by sudden head movement. For example, benign paroxysmal positional vertigo (BPPV), a common cause of dizziness,44 is thought to occur when small crystals of calcium carbonate (otoconia) from the utricular macula (the “linear accelerometers” of the vestibular labyrinth) migrate into the semicircular canal system. Changes in head position with regard to gravity, particularly in pitch or roll, trigger movement of the otoconia within the canal, generating vertigo and ocular nystagmus. In the acute phase of recovery after vestibular nerve sections or acoustic neuroma resections, patients often have brief episodes of vertigo during head rotations with associated blurred vision.45 In contrast, Ménière’s disease is characterized by sudden and unexpected attacks of vertigo usually unrelated to head movement.46
Vestibular impairment engenders a substantial reliance on visual information to maintain balance and gaze, and a suppression of head movement to prevent vertigo and inappropriate ocular nystagmus, which may affect the ability of the individual to drive. Moreover, the vestibulo-ocular reflex (VOR), which acts to stabilize gaze by generating compensatory eye movements in the opposite direction to head movement, is critical in maintaining dynamic visual acuity while driving, because even the paved roads of urban centers generate high-frequency perturbations of the head. Clinicians in a number of countries have expressed significant concern with regard to vestibular disease and driving,47–50 although to date, there has been little functional research in this area. A recent study by Cohen et al45 assessed the impact of vestibular dysfunction on driving performance from subjective reports using a modified form of the Driving Habits Questionnaire developed for patients with visual impairment.8 The subject pool represented a broad spectrum of vestibular disorders, including BPPV, postoperative acoustic neuroma resections and vestibular nerve sections, Ménière’s disease, and chronic idiopathic vestibulopathy. These subjects reported considerable difficulty driving in reduced visibility (such as at night or during rain) and in visually complex environments (high-traffic roads, large intersections). Complex maneuvers involving spatial navigation such as changing lanes, staying in lane, and parking were problematic, likely as a result of impairment of path integration (i.e., summing of self and vehicle movement over time) that is degraded in vestibular patients.51 Vertigo and ocular nystagmus were also triggered by tasks requiring rapid head motion such as checking for traffic before changing lanes or entering an intersection or freeway on-ramp.45 The impact on stable vision can be devastating, because inappropriate nystagmus (i.e., not compensatory for head rotation) causes “spinning” of the visual scene.
Head–eye coordination is also critical in assessing the impact of visual field deficits as a result of ocular abnormalities52, 53 (macular degeneration, glaucoma, or retinitis pigmentosa) or brain lesions (such as hemianopsia, a loss of vision in half of the visual field often related to stroke,54–56 or head trauma). Visual field loss does not affect visual identification but alters search strategies,57 with longer scan paths, more frequent and prolonged fixations, and consequently increased error.58 Transportation authorities in the United States and Europe typically require a minimum 120° horizontal field of view in the better functioning eye to obtain a driving license. However, recent studies have suggested that many patients who do not meet this criterion are able to drive safely with appropriate adaptation of head–eye coordination. Of 35 subjects with peripheral visual field deficits resulting from ocular disease (with a horizontal field extent limited to 84° on average), 15 (43%) passed an on-road driving assessment.52 Furthermore, patients who passed the test were found to have performed a greater number of head movements and began scanning earlier when approaching an intersection than those who failed. A similar study was recently performed with hemianopic patients whose visual field is typically limited to 90°.59 Training patients to perform frequent, large horizontal saccades into the blind hemifield, while minimizing head movement, generated a subjective enhancement of vision and measurable improvement in driving ability.59
Analysis of head–eye coordination and vestibular contributions to visual behavior while driving is important in establishing standard operator performance, and for assessment of drivers with vestibular and visual field impairment. In this article, we describe a novel laptop-based system for simultaneous acquisition of three-dimensional (3D) head, eye, and vehicle movement in real time. The system is comprised entirely of commercially available hardware and uses established VOG algorithms to accurately determine horizontal and vertical movements of the pupil, as well as rotation of the eye around the line of sight (ocular torsion).20, 21 The portability of the system allows for the functional study of head–eye coordination and the vestibulo-ocular reflex in situations that were not previously possible, and as examples, we provide data obtained from a pilot operating an Airbus A340-600 motion simulator and from a subject driving a car in Manhattan.
The experiments described subsequently were approved by the Institutional Review Board of the Mount Sinai School of Medicine and conformed to the Declaration of Helsinki. Informed consent was obtained from all subjects.
The head–eye tracker was developed as part of a NASA-funded study of spatial disorientation in shuttle pilots60 and was implemented using commercially available components. Two “firewire” (IEEE 1394) digital cameras (Firefly; Point Gray Research, BC, Canada) were attached to lightweight swimming goggles (Aquasphere Seal, Genova, Italy) (Fig. 1A, B). The total weight of the headset was 146 g, significantly lighter than commercially available VOG goggles. A limitation of the current system is that spectacles cannot be worn under the goggles (although contact lenses can be used at the expense of torsional eye position measurement accuracy), but the use of a head-band arrangement to mount the cameras resolves this issue. The left eye was illuminated by an IR light-emitting diode (HSDL-4220; Hewlett-Packard, Houston, TX), and the image of the eye was directed onto the left camera through a dichroic mirror (Wideband Hot Mirror, OCLI, CA) that was sensitive to light in the IR band but allowed visible light to pass through, providing a clear field of view. A second camera was directed along the naso-occipital axis, providing images of the scene from the subject’s point of view. Note that this camera could also be used to obtain binocular images of the eyes. The camera cables (Apple Thin Firewire Cable; Apple Computer Inc., CA) were connected to a PCMIA firewire card (IEEE-1394 CardBus PC Card CBFW3U; Ratoc Systems International, San Jose, CA) inserted into the PC slot of the laptop computer (SONY Vaio PCG-GRV670P) and powered from the laptop battery. The use of digital rather than analog video cameras eliminated the need for a video acquisition card, because digital images of the eye were provided directly from the cameras.
Eye movements were calibrated by having the subject view targets generated by a goggle-mounted laser (class 3A <5 mw 635 nm visible laser diode module) with a diffraction grating (Laser X-Hair Generating Optic, Lasermate Group Inc., Pomona, CA), which projected a “cross” pattern of lines that subtended a visual angle of ±11° horizontally and vertically and were fixed with respect to the head. Head and vehicle movement were determined with inertial measurement units (IMUs), connected to the laptop USB port, which used triaxial accelerometers and angular rate sensors to measure 3D linear acceleration and angular velocity in space. In addition, yaw, pitch, and roll orientation of the head and vehicle in space was calculated from the IMU linear acceleration and angular velocity data using three integral flux gate compasses and temperature compensation to suppress drift (MT9; Xsens, Enschede, The Netherlands). Addition of a USB analog/digital data acquisition interface (PMD-1208LS USB-based DAQ module; Measurement Computing Corp., MA) allowed auxiliary analog or digital data to be collected simultaneously. As of January 2005, the total cost of the hardware was less than U.S. $6000.
The system software was written in Labview G (National Instruments, Austin, TX). Images of the left eye and scene were acquired at a rate of 30 Hz,b and the center of the pupil was determined using a “center-of-mass” algorithm.20, 21 Horizontal and vertical eye position were calculated in Fick coordinates using a spherical model of the eye,21 the radius of which was calculated using a calibration procedure in which the subject fixated targets at known gaze angles generated by the head-referenced laser. The laser display was also visible in the scene image to calibrate gaze, and total setup time was <5 min. Torsional eye position was calculated using polar crosscorrelation,16, 18–21 in which pixels within the iris are sampled along elliptical annuli centered on the pupil and crosscorrelation of these signals provides the amount of relative rotation about the line of sight between two images.20–22 These algorithms have demonstrated an accuracy and resolution of the order of 0.1°.21
The three dimensions of eye position (horizontal, vertical, and torsional), head and vehicle movement, and analog or digital data logged with the data acquisition card were presented in real time on the laptop screen (Fig. 1C). In addition to providing eye position data, the point of regard was superimposed on the corresponding scene image to allow analysis of patterns of gaze in real time (Fig. 1C). Although commercial desktop-based VOG systems with scene cameras have been available in recent years, our system is the first to integrate 3D eye-in-head position, 6 degrees-of-freedom head and vehicle movement, plus gaze-in-scene in real time within a laptop-based unit suitable for use in vehicles and simulators.
Flight Motion Simulator
The system was used to acquire eye, head, and cabin movement data aboard a full flight motion simulator (A340-600) at the Airbus training facility in Toulouse, France. Figure 2 shows roll head and eye movements during sustained (30 s) 45° banking turns during a fixed-base simulator run that modeled the heading alignment circle (HAC) maneuver during the final approach of the space shuttle.60 In response to the tilt of the visual horizon, there was a maintained tilt of the head of up to 5° (Fig. 2: lower panel, solid line), termed the optokinetic cervical reflex,61, 62 although in this instance, it is likely a combined optokinetic and optostatic response. In addition, there was a sustained torsional shift in eye position of 6° (Fig. 2: lower panel, dashed line), which preceded the head tilt. This ocular torsion was of similar magnitude to that produced by the gravity-sensing otoliths during a 45° head tilt with regard to gravity (ocular counterrolling [OCR]).63 It is important to note, however, that the OCR reflex would rotate the eye in the opposite direction to head tilt, whereas in this instance, both the head and eye rotated toward the scene-derived “visual vertical” (i.e., perpendicular to the horizon). Ocular torsion in response to a rotating visual line has recently been described,64 and this is, to our knowledge, the first account of sustained ocular torsion in response to a statically tilted scene. The combined head and eye roll tilt acted to orient the eye to the scene-derived visual vertical with a gain of approximately 25%.
Driving a Car
Data were obtained from an experienced subject (licensed 23 years) driving a midsized four-door sedan (2000 Chevrolet Cavalier LS) in daylight in a dense, urban environment (Manhattan). Figure 3 shows a sequence of images as the driver negotiated a series of 180° turns in a multilevel car park. The analysis software was set to output gaze fixations ±200 ms either side of the current scene image to allow complex scanning patterns to be represented in a static image. The subject scanned the reverse and brake lights on the rear of each parked vehicle ahead in a linear manner with a fixation time of approximately 100 ms per vehicle (i.e., on average four cars were scanned in each panel of Fig. 3, which represents a period of 400 ms). When a vehicle partially reversed into his path, the driver maintained fixation on the lit reverse indicator for a longer period (Fig. 3: panels 5 and 6) while continuing to scan the cars ahead. A similar scanning strategy continued when turning right from the car park onto the street (Fig. 4A). Other scanning behaviors included checking the position of pedestrians while preparing to make a righthand turn (Fig. 4B), estimating the gap between two parked trucks before passing (Fig. 4C), reading an overhead road sign (Fig. 4D), fixating alternately on the tangent point of a curving road11, 31 and the car ahead (Fig. 4E), scanning the rearview mirror and the road ahead (Fig. 4F), and checking the lefthand side mirror and a van in the driver’s blind spot before changing lanes to the left (Fig. 4G, H).
A novel aspect of our system is that it seamlessly provides synchronous head and vehicle movement data to augment the eye-in-head position data and gaze-in-scene. As the driver negotiated a twisting off-ramp from the George Washington Bridge (Fig. 5), both the fixation of the tangent of the curve and the roll tilt of the head into the turn are clearly shown, as described in two previous separate studies.12,33 The tilt of the head averaged approximately 10° into the turn (Fig. 5: lower panel, solid line), which was approximately half the angle of the gravito-inertial accelerationc (GIA) tilt (Fig. 5: lower panel, dashed line). The magnitude of head and GIA tilt were similar to that observed in humans walking around a 0.5-m radius turn.32
During unpredictable passive head movement, the vestibulo-ocular reflex (VOR) stabilizes gaze through compensatory eye movements. Figure 6 shows 15 s of head and eye pitch (rotation about axes parallel to the interaural axis) while driving along West 96th Street on Manhattan’s upper west side. Although this was a paved road in reasonably good condition, there was a continuous high-frequency pitching of the head (with peak-to-peak amplitude of approximately 6°) as a result of the vertical movement of the automobile over the uneven road surface (Fig. 6A: solid line). The pitch of the head, sensed by the semicircular canals, was used to generate vertical eye movements by the VOR that were of similar amplitude but in the opposite direction to head movement to maintain gaze (Fig. 6A: dashed line). A scatterplot of eye versus head position demonstrates this compensatory reflex with a linear regression showing a slope of close to unity (0.9) (Fig. 6B). Thus, the VOR is constantly generating compensatory eye movement to overcome passive perturbations of the head, which augments active gaze fixations to maintain stable vision when driving.
Driving a car is a visually complex undertaking, requiring integration of both active and reflexive head and eye movements to provide a stable view of the surroundings. Human head—eye coordination did not evolve to suit the high-inertial environment of powered vehicles, and these patterns of behavior must be learned, as evidenced by the limited scanning strategies of novice drivers.3, 4 Moreover, visual impairment, which may not greatly affect active locomotion, can significantly diminish the acquired skill of automobile control.7, 8, 10, 45 Obtaining functional measures of head–eye coordination during vehicle operation is an important aspect in understanding the basic visual strategies underlying driving, as well as assessment of the impact of visual and vestibular impairment on driving performance. Our laptop-based system allows the real-time analysis of 3D head–eye coordination that incorporates both active head and eye fixation strategies, plus reflex contributions from the vestibulo-collic and vestibulo-ocular reflexes. The accuracy of the system is such that complex visual behaviors can be identified and quantified with a high temporal and spatial resolution.
To date, there has been little emphasis on the vestibular aspects of driving, at least in the functional sense presented here, but this is clearly an important issue. The VOR is continuously operational while driving, underpinning the active visual scanning strategies described here. Although patients with vestibulopathy may overcome decrements in VOR performance to some extent with pursuit and saccadic eye movements, this strategy breaks down in poor visual conditions or complex environments and during rapid head motion.45 As an example, the seemingly straightforward task of exiting a car park involves the methodical scanning of parked cars at rates of up to 10 per second (Fig. 3), which must be accomplished during rapid turns of the vehicle and the accompanying vestibular-generated head and eye movements to provide a seamless view of an environment with many potential hazards. A large number of drivers with vestibular impairment report significant difficulty in negotiating multistory car parks.45 Inappropriate ocular nystagmus, and therefore paradoxic motion of the visual surround, can be induced by activation of the semicircular canals during turns of the head and vehicle. In addition, the visual structure of car parks, which often have evenly spaced vertical columns or blinds to allow natural light, can present a challenge. Unfortunately, for drivers with vestibulopathy, the horizontal motion of these vertical structures while turning are akin to a rotating optokinetic drum45 and can themselves induce episodes of vertigo and optokinetic nystagmus.d
The integration of gaze analysis with head and vehicle movement allows the evaluation of vestibular reflexes that maintain stable vision while driving. This may prove a useful adjunct to purely subjective forms of assessment of vestibulopathy on driving ability45 as well as addressing the larger question of what level of vestibular impairment is sustainable for safe driving.47–50 For the car park example presented here, episodes of vertigo with accompanying nystagmus can be identified and linked to specific head or vehicle movements or features of the visual environment. A similar approach could be used to establish driving maneuvers involving head motion that are of particular concern such as changing lanes (Fig. 4H), cornering (Fig. 5), and driving on rough road surfaces (Fig. 6), and potentially lead to modifications in driving behavior to limit disorientation. This approach is used in vestibular rehabilitation such as advising patients with BPPV to avoid placing objects on high shelves in the home to minimize head pitch movements that trigger vertigo. It is interesting to note that of the four vestibular patient populations in the Cohen study,45 those with BPPV reported the lowest incidence of driving difficulty. Because their vertigo is linked to specific active head movements in the roll or pitch plane, it is likely that these patients have adapted their head–eye coordination strategy to circumvent an on-road attack. A functional assessment of head and eye movement during driving would be a useful means to investigate this hypothesis.
The dangers of driving with poor vision are well documented,7, 8, 10 and although our system cannot determine how well a driver sees the world, it can be used to assess the effects of ocular degeneration on the visual performance of drivers in a functional setting. Analysis of head–eye coordination could be useful in assessment of drivers with visual neglect or visual field deficits, which require a change in scanning strategies to improve vision.58, 59, 65, 66 Recent studies have suggested that adaptation of head–eye coordination by patients with visual field deficits can improve vehicle operation,52, 59 even to the extent of passing an on-road driving assessment,52 despite the fact that the horizontal visual extent in these individuals officially precludes them from obtaining a license. Thus, training patients to adapt head–eye coordination to extend their functional horizontal field, possibly in conjunction with the use of auxiliary optical aids (such as prisms65, 66), may improve the quality of life of many individuals currently deemed unfit to drive.
A detailed analysis of head–eye coordination and visual search strategies may also be of use in driver training. The more sophisticated scanning patterns of experienced drivers3, 4 are presumably learned over many years on the road, and this information could be used in the instruction of novice drivers. This objective approach to teaching vehicle control based on the visual strategies of experienced operators has been evaluated by the U.S. Air Force67 and is currently being investigated by commercial aviation and NASA using the apparatus described here.60
Supported by NASA grants NCC 9–128 and NNJ04HF51G (Steven Moore) and a National Space Biomedical Research Institute (NSBRI) postdoctoral fellowship (Hamish MacDougall).
Steven T. Moore, PhD
Mt. Sinai School of Medicine
Neurology Dept., Box 1135
1 E 100th St., New York NY 10029
1. National Safety Council. Injury Facts: 2003. Itasca, IL: National Safety Council; 2003.
3. Crundall DE, Underwood G. Effects of experience and processing demands on visual information acquisition in drivers. Ergonomics 1998;41:448–58.
4. Underwood G, Chapman P, Brocklehurst N, Underwood J, Crundall D. Visual attention while driving: sequences of eye fixations made by experienced and novice drivers. Ergonomics 2003;46: 629–46.
5. Wood JM, Mallon K. Comparison of driving performance of young and old drivers (with and without visual impairment) measured during in-traffic conditions. Optom Vis Sci 2001;78:343–9.
6. Wood JM. Aging, driving and vision. Clin Exp Optom 2002;85: 214–20.
7. Owsley C, Ball K, McGwin G, Sloane ME, Roenker DL, White MF, Overley ET. Visual processing impairment and risk of motor vehicle crash among older adults. JAMA 1998;279:1083–8.
8. Owsley C, Stalvey B, Wells J, Sloane ME. Older drivers and cataract: driving habits and crash risk. J Gerontol A Biol Sci Med Sci 1999;54: M203–11.
9. Owsley C, Stalvey BT, Wells J, Sloane ME, McGwin G Jr. Visual risk factors for crash involvement in older drivers with cataract. Arch Ophthalmol 2001;119:881–7.
10. Wood JM. Age and visual impairment decrease driving performance as measured on a closed-road circuit. Hum Factors 2002;44:482–94.
11. Land MF, Lee DN. Where we look when we steer. Nature 1994;369: 742–4.
12. Land M, Horwood J. Which parts of the road guide steering? Nature 1995;377:339–40.
13. Kito T, Haraguchi M, Funatsu T, Sato M, Kondo M. Measurements of gaze movements while driving. Percept Mot Skills 1989;68:19–25.
14. Young LR, Sheena D. Eye-movement measurement techniques. Am Psychol 1975;30:315–30.
15. Samel A, Diedrich A, Drescher J, Lorenz B, Plath G, Vejvoda M, Wenzel J. [Long-term monitoring of psychophysiologic values in flight physiology.] Internist (Berl) 1997;38:755–69.
16. Hatamian M, Anderson DJ. Design considerations for a real-time ocular counterroll instrument. IEEE Trans Biomed Eng 1983;30: 278–88.
17. Parker JA, Kenyon RV, Young LR. Measurement of torsion from multitemporal images of the eye using digital signal processing techniques. IEEE Trans Biomed Eng 1985;32:28–36.
18. Clarke AH, Teiwes W, Scherer H Videooculography: an alternative method for measurement of three-dimensional eye movements. In: Schmid R, Zambarbieri D, eds. Oculomotor Control and Cognitive Processes: Normal and Pathological Aspects. Amsterdam: North- Holland; 1991:431–43.
19. Clarke AH, Ditterich J, Druen K, Schonfeld U, Steineke C. Using high frame rate CMOS sensors for three-dimensional eye tracking. Behav Res Methods Instrum Comput 2002;34:549–60.
20. Moore ST, Curthoys IS, McCoy SG. VTM–an image-processing system for measuring ocular torsion. Comput Methods Programs Biomed 1991;35:219–30.
21. Moore ST, Haslwanter T, Curthoys IS, Smith ST. A geometric basis for measurement of three-dimensional eye position using image processing. Vision Res 1996;36:445–59.
22. Haslwanter T, Moore ST. A theoretical analysis of three-dimensional eye position measurement using polar cross-correlation. IEEE Trans Biomed Eng 1995;42:1053–61.
23. Bos JE, de Graaf B. Ocular torsion quantification with video images. IEEE Trans Biomed Eng 1994;41:351–7.
24. Groen E, Bos JE, Nacken PF, de Graaf B. Determination of ocular torsion by means of automatic pattern recognition. IEEE Trans Biomed Eng 1996;43:471–9.
25. Imai T, Takeda N, Morita M, Koizuka I, Kubo T, Miura K, Nakamae K, Fujioka H. Rotation vector analysis of eye movement in three dimensions with an infrared CCD camera. Acta Otolaryngol 1999; 119:24–8.
26. Zhu D, Moore ST, Raphan T. Robust pupil center detection using a curvature algorithm. Comput Methods Programs Biomed 1999;59: 145–57.
27. Zhu D, Moore ST, Raphan T. Robust and real-time torsional eye position calculation using a template-matching technique. Comput Methods Programs Biomed 2004;74:201–9.
28. Cheung B, Hofer K. Eye tracking, point of gaze, and performance degradation during disorientation. Aviat Space Environ Med 2003; 74:11–20.
29. Geruschat DR, Hassan SE, Turano KA. Gaze behavior while crossing complex intersections. Optom Vis Sci 2003;80:515–28.
30. Vivekananda-Schmidt P, Anderson RS, Reinhardt-Rutland AH, Shields TJ. Simulated impairment of contrast sensitivity: performance and gaze behavior during locomotion through a built environment. Optom Vis Sci 2004;81:844–52.
31. Land MF, Tatler BW. Steering with the head. The visual strategy of a racing driver. Curr Biol 2001;11:1215–20.
32. Imai T, Moore ST, Raphan T, Cohen B. Interaction of the body, head, and eyes during walking and turning. Exp Brain Res 2001;136: 1–18.
33. Zikovitz DC, Harris LR. Head tilt during driving. Ergonomics 1999; 42:740–6.
34. Friedman JM. Post-traumatic vertigo. Med Health R I 2004;87: 296–300.
35. Johnson RT. Vestibular neuritis, or driving dizzily through Donegal. N Engl J Med 2004;351:322–3.
36. Lee H, Ahn BH, Baloh RW. Sudden deafness with vertigo as a sole manifestation of anterior inferior cerebellar artery infarction. J Neurol Sci 2004;222:105–7.
37. Black FO, Pesznecker S, Stallings V. Permanent gentamicin vestibulotoxicity. Otol Neurotol 2004;25:559–69.
38. Paige GD. Senescence of human visual–vestibular interactions. 1. Vestibulo-ocular reflex and adaptive plasticity with aging. J Vestib Res 1992;2:133–51.
39. Cohen H, Heaton LG, Congdon SL, Jenkins HA. Changes in sensory organization test scores with age. Age Ageing 1996;25:39–44.
40. Nakayama M, Helfert RH, Konrad HR, Caspary DM. Scanning electron microscopic evaluation of age-related changes in the rat vestibular epithelium. Otolaryngol Head Neck Surg 1994;111: 799–806.
41. Rosenhall U. Degenerative patterns in the aging human vestibular neuro-epithelia. Acta Otolaryngol 1973;76:208–20.
42. Lyon MJ, Wanamaker HH. Blood flow and assessment of capillaries in the aging rat posterior canal crista. Hear Res 1993;67:157–65.
43. Lopez I, Honrubia V, Baloh RW. Aging and the human vestibular nucleus. J Vestib Res 1997;7:77–85.
44. Katsarkas A. Benign paroxysmal positional vertigo (BPPV): idiopathic versus post-traumatic. Acta Otolaryngol 1999;119:745–9.
45. Cohen HS, Wells J, Kimball KT, Owsley C. Driving disability and dizziness. J Safety Res 2003;34:361–9.
46. Minor LB, Schessel DA, Carey JP. Ménière’s disease. Curr Opin Neurol 2004;17:9–16.
47. McKiernan D, Jonathan D. Driving and vertigo. Clin Otolaryngol 2001;26:1–2.
48. Parnes LS, Sindwani R. Impact of vestibular disorders on fitness to drive: a census of the American Neurotology Society. Am J Otol 1997;18:79–85.
49. Sindwani R, Parnes LS. Reporting of vestibular patients who are unfit to drive: survey of Canadian otolaryngologists. J Otolaryngol 1997; 26:104–11.
50. Sindwani R, Parnes LS, Goebel JA, Cass SP. Approach to the vestibular patient and driving: a patient perspective. Otolaryngol Head Neck Surg 1999;121:13–7.
51. Cohen HS. Vestibular disorders and impaired path integration along a linear trajectory. J Vestib Res 2000;10:7–15.
52. Coeckelbergh TR, Brouwer WH, Cornelissen FW, Van Wolffelaar P, Kooijman AC. The effect of visual field defects on driving performance: a driving simulator study. Arch Ophthalmol 2002;120: 1509–16.
53. Coeckelbergh TR, Cornelissen FW, Brouwer WH, Kooijman AC. The effect of visual field defects on eye movements and practical fitness to drive. Vision Res 2002;42:669–77.
54. Ross JV. Bilateral homonymous hemianopsia caused by vascular occlusive disease. Am J Ophthalmol 1949;32:1588.–
55. Kearns TP, Wagener HP, Millikan CH. Bilateral homonymous hemianopsia; relationship to thrombosis of the basilar artery. AMA Arch Ophthalmol 1954;53:560–5.
56. Reese FM. Bilateral homonymous hemianopsia. Am J Ophthalmol 1954;38:44–57.
57. Zihl J, Wohlfarth-Englert A. The influence of visual field disorders on visual identification tasks. Eur Arch Psychiatry Neurol Sci 1986;236: 61–4.
58. Tant ML, Cornelissen FW, Kooijman AC, Brouwer WH. Hemianopic visual field defects elicit hemianopic scanning. Vision Res 2002;42:1339–48.
59. Tant ML. Visual performance in homonymous hemianopia: assessment, training and driving [PhD Thesis]. Department of Psychology, Division Biopsychology and Neuropsychology, University of Groningen; 2002.
60. Moore ST, MacDougall H, Clark JB, Wuyts F, Lesceu X, Speyer JJ, Cohen B. Spatial disorientation—how the brain interprets linear acceleration during flight. J Vest Res 2004;14:114.
61. Gallimore JJ, Brannon NG, Patterson FR, Nalepka JP. Effects of FOV and aircraft bank on pilot head movement and reversal errors during simulated flight. Aviat Space Environ Med 1999;70:1152–60.
62. Gallimore JJ, Patterson FR, Brannon NG, Nalepka JP. The opto-kinetic cervical reflex during formation flight. Aviat Space Environ Med 2000;71:812–21.
63. Moore ST, Clement G, Raphan T, Cohen B. Ocular counterrolling induced by centrifugation during orbital space flight. Exp Brain Res 2001;137:323–35.
64. Mezey LE, Curthoys IS, Burgess AM, Goonetilleke SC, MacDougall HG. Changes in ocular torsion position produced by a single visual line rotating around the line of sight—visual ‘entrainment’ of ocular torsion. Vision Res 2004;44:397–406.
65. Angeli V, Benassi MG, Ladavas E. Recovery of oculo-motor bias in neglect patients after prism adaptation. Neuropsychologia 2004;42: 1223–34.
66. Angeli V, Meneghello F, Mattioli F, Ladavas E. Mechanisms underlying visuo-spatial amelioration of neglect after prism adaptation. Cortex 2004;40:155–6.
67. Wetzel PA, Anderson GM, Barelka BA. Instructor use of eye position based feedback for pilot training. In: Human Factors and Ergonomics Society 42nd Annual Meeting, Chicago, IL Oct. 5–9, 1998. Proceedings, vol 2. Santa Monica, CA: Human Factors and Ergonomics Society; 1998:abstract A99-1412602-54.
aOne minute of videotape comprises 1500 (PAL) to 1800 frames (NTSC). Cited Here...
bPreliminary trials of a new generation of firewire cameras (Scorpion, Point Grey Research, BC Canada) demonstrated a sample rate of 150 Hz. Cited Here...
cThe vector sum of gravity and the centripetal acceleration of the car as it rounded the bend. Cited Here...
dThe optokinetic reflex generates ocular nystagmus in response to movement of the visual surround and shares many of the same neural pathways as the vestibulo-ocular reflex. Cited Here...
Keywords:© 2005 American Academy of Optometry
head tracking; eye movements; vestibular; driving; flight simulator