Advances in virtual reality technology present new opportunities for human factors research in areas that are dangerous, difficult, or expensive to study in the real world. The authors developed a ...new pedestrian simulator using the HTC Vive head mounted display and Unity software. Pedestrian head position and orientation were tracked as participants attempted to safely cross a virtual signalized intersection (5.5 m). In 10% of 60 trials, a vehicle violated the traffic signal and in 10.84% of these trials, a collision between the vehicle and the pedestrian was observed. Approximately 11% of the participants experienced simulator sickness and withdrew from the study. Objective measures, including the average walking speed, indicate that participant behavior in VR matches published real world norms. Subjective responses indicate that the virtual environment was realistic and engaging. Overall, the study results confirm the effectiveness of the new virtual reality technology for research on full motion tasks.
•Advances in virtual reality technology present new opportunities for human factors research.•The HTC Vive head mounted display would be an effective tool for pedestrian behavior research on full motion tasks.•Subjective responses indicated that the virtual environment was realistic and engaging.•Objective measures indicated that participant behavior in VR matches previously published real world norms.
A head tracker is a crucial part of the head-mounted display systems, as it tracks the head of the pilot in the plane/cockpit simulator. The operational flaws of head trackers are also dependent on ...different environmental conditions like different lighting conditions and stray light interference. In this paper, an optical tracker has been employed to gather the 6-DoF data of head movements under different environmental conditions. Also, the effect of different environmental conditions and variation in distance between the receiver and optical transmitter on the 6-DoF data is analyzed. This can help in the prediction of the accuracy of a optical head tracker under different environmental conditions prior to its deployment in the aircraft.
The elderly population is increasing rapidly all over the world. One major risk for elderly people is fall accidents, especially for those living alone. In this paper, we propose a robust fall ...detection approach by analyzing the tracked key joints of the human body using a single depth camera. Compared to the rivals that rely on the RGB inputs, the proposed scheme is independent of illumination of the lights and can work even in a dark room. In our scheme, a pose-invariant randomized decision tree algorithm is proposed for the key joint extraction, which requires low computational cost during the training and test. Then, the support vector machine classifier is employed to determine whether a fall motion occurs, whose input is the 3-D trajectory of the head joint. The experimental results demonstrate that the proposed fall detection method is more accurate and robust compared with the state-of-the-art methods.
Immersive listening systems have grown significantly over the past decade and are now an established area of scientific, artistic, and industrial research. However, scarce research has been conducted ...on musicians' preferences for playing through headphones over binaural spatialization systems with the addition of head tracking, as opposed to classical stereophonic systems. This comparison is essential to optimally support the playing experience with others for cases of remote collaborative playing, individual instrumental practice, individual recreational music-making using backing tracks, and studio recording sessions. In this article, we study the preferences of playing musicians for a stereophonic system versus a binaural head-tracking system composed of Ambisonics technology and binaural synthesis with generalized head-related transfer functions. We conducted two experiments, each with 30 expert musicians, where participants were asked to rate and compare the 2 listening conditions while playing their instrument either seated or standing. Overall, the quantitative and qualitative results indicated a generalized preference for the binaural system with head tracking over the stereophonic system, with higher ratings for localization, immersion, social presence, realism, and connection with other musicians. Moreover, participants moved their heads significantly more in the binaural conditions. This phenomenon may be explained by the higher engagement and arousal due to the improved auditory experience, or alternatively by the presence of embodied music cognition mechanisms that cause a higher degree of exploration to better understand the action-perception loop. These findings highlight the need for progressing current commercial hardware and software systems used by musicians while playing over headphones.
This paper reviews the recent development of augmented reality (AR) head-tracking techniques. AR is an emerging technology that provides users with digital augmentation layered on the real-life ...environment. AR systems use tracking to maintain the point of reference and enable the device to follow the user's movements and position within the physical world. Head-tracking is a crucial component of AR that inputs and interprets the movement of the device and the user's physical location in real-time. The main challenge in head-tracking is the appearance change that results from head rotation, which necessitates high accuracy and long-range tracking in a noisy environment. Latency and errors in head-tracking can reduce the system's overall effectiveness and cause jarring or unsettling effects for the user. This paper's contribution is to shed light on the best methods for tracking and registering in augmented reality, which will guide future advancements in the field. The review covers recent work proposed to improve head-tracking performance and reduce latency, highlighting the challenges and future directions of head-tracking techniques in AR.
Motion perception is a critical function of the visual system. In a three-dimensional environment, multiple sensory cues carry information about an object's motion trajectory. Previous work has ...quantified the contribution of binocular motion cues, such as interocular velocity differences and changing disparities over time, as well as monocular motion cues, such as size and density changes. However, even when these cues are presented in concert, observers will systematically misreport the direction of motion-in-depth. Although in the majority of laboratory experiments head position is held fixed using a chin or head rest, an observer's head position is subject to involuntary small movements under real-world viewing conditions. Here, we considered the potential impact of such "head jitter" on motion-in-depth perception. We presented visual stimuli in a head-mounted virtual reality device that facilitated low latency head tracking and asked observers to judge 3D object motion. We found performance improved when we updated the visual display consistent with the small changes in head position. When we disrupted or delayed head movement-contingent updating of the visual display, the proportion of motion-in-depth misreports again increased, reflected in both a reduction in sensitivity and an increase in bias. Our findings identify a critical function of head jitter in visual motion perception, which has been obscured in most (head-fixed and non-head jitter contingent) laboratory experiments.
Head pose estimation (HPE) is currently a growing research field, mainly because of the proliferation of human–computer interfaces (HCI) in the last decade. It offers a wide variety of applications, ...including human behavior analysis, driver assistance systems or gaze estimation systems. This article aims to contribute to the development of robust and accurate HPE methods based on 2D tracking of the face, enhancing performance of both 2D point tracking and 3D pose estimation. We start with a baseline method for pose estimation based on POSIT algorithm. A novel weighted variant of POSIT is then proposed, together with a methodology to estimate weights for the 2D–3D point correspondences. Further, outlier detection and correction methods are also proposed in order to enhance both point tracking and pose estimation. With the aim of achieving a wider impact, the problem is addressed using a global approach: all the methods proposed are generalizable to any kind of object for which an approximate 3D model is available. These methods have been evaluated for the specific task of HPE using two different head pose video databases; a recently published one that reflects the expected performance of the system in current technological conditions, and an older one that allows an extensive comparison with state-of-the-art HPE methods. Results show that the proposed enhancements improve the accuracy of both 2D facial point tracking and 3D HPE, with respect to the implemented baseline method, by over 15% in normal tracking conditions and over 30% in noisy tracking conditions. Moreover, the proposed HPE system outperforms the state of the art on the two databases.
•We present a novel, robust and accurate head pose estimation algorithm.•Our methodology is applicable to any object if we have an approximate 3D model.•We successfully address the problem of drifting of points that lose track, inherent to most point-tracking systems.•We validate all the enhancements proposed on two different databases of videos.•Our final method outperforms the state of the art on the two test databases.
•We present a new public database of videos for head tracking and pose estimation.•We provide ground-truth data with lower noise than other similar frameworks.•We have developed an automatic face ...annotation procedure with negligible error.•We show the utility of the database for training and evaluation of algorithms.•We carry out a thorough comparison between state-of-the-art head tracking methods.
A new public database of videos for head tracking and pose estimation is presented in this paper with the goal of establishing a new framework for algorithm validation, replacing out of date frameworks. Position data has been recorded with a magnetic sensor-transmitter that has previously been aligned and synchronized with a commercial webcam, and we provide reliable ground-truth for 3D rotation and translation of the head with respect to the camera. In addition to this, an automatic face annotation procedure has been developed, which provides the image position of 54 facial landmarks, with negligible error, in every video frame in the database. This image ground-truth can be used for algorithm training or head tracking evaluation, among others. In order to show the usability of the database, we evaluate three head tracking approaches and three head models, and combine them to provide nine different head pose estimation sets of results. We show the validity of the presented database both for training and evaluation of head tracking and pose estimation methods, and provide an interesting comparison in performance of state-of-the-art algorithms. These results may also serve as reference to encourage other researchers to train and test their algorithms with this database, and compare their results with the ones presented in this paper.
•We propose a robust registration method for real-time head tracking in the MR scanner with high temporal resolution.•We aggregate motion estimates into a per-sequence average score and show that ...motion affects T1-weighted image quality, even for predominantly healthy, compliant participants.•In three evaluations, our registration method outperforms the vendor-supplied method with increased similarity to fMRI motion traces, improved recovery of an independently recorded breathing signal, and higher correlation with structural MRI quality estimates.•We present a strong association of increased motion with increasing age and body mass index, as well as longitudinally with scan session duration in the Rhineland study - a large population cohort.•We observe a high correlation of motion scores across sequences, hinting at the possibility to employ fMRI derived motion scores as a surrogate to control motion in structural neuromorphometric statistical analyses.
Head motion during MR acquisition reduces image quality and has been shown to bias neuromorphometric analysis. The quantification of head motion, therefore, has both neuroscientific as well as clinical applications, for example, to control for motion in statistical analyses of brain morphology, or as a variable of interest in neurological studies. The accuracy of markerless optical head tracking, however, is largely unexplored. Furthermore, no quantitative analysis of head motion in a general, mostly healthy population cohort exists thus far. In this work, we present a robust registration method for the alignment of depth camera data that sensitively estimates even small head movements of compliant participants. Our method outperforms the vendor-supplied method in three validation experiments: 1. similarity to fMRI motion traces as a low-frequency reference, 2. recovery of the independently acquired breathing signal as a high-frequency reference, and 3. correlation with image-based quality metrics in structural T1-weighted MRI. In addition to the core algorithm, we establish an analysis pipeline that computes average motion scores per time interval or per sequence for inclusion in downstream analyses. We apply the pipeline in the Rhineland Study, a large population cohort study, where we replicate age and body mass index (BMI) as motion correlates and show that head motion significantly increases over the duration of the scan session. We observe weak, yet significant interactions between this within-session increase and age, BMI, and sex. High correlations between fMRI and camera-based motion scores of proceeding sequences further suggest that fMRI motion estimates can be used as a surrogate score in the absence of better measures to control for motion in statistical analyses.
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