Conflicts between short- and long-term experiences affect visual perception through modulating sensory or motor response systems: Evidence from Bayesian inference models.

The independent effects of short- and long-term experiences on visual perception have been discussed for decades. However, no study has investigated whether and how these experiences simultaneously affect our visual perception. To address this question, we asked participants to estimate their self-motion directions (i.e., headings) simulated from optic flow, in which a long-term experience learned in everyday life (i.e., straight-forward motion being more common than lateral motion) plays an important role. The headings were selected from three distributions that resembled a peak, a hill, and a flat line, creating different short-term experiences. Importantly, the proportions of headings deviating from the straight-forward motion gradually increased in the peak, hill, and flat distributions, leading to a greater conflict between long- and short-term experiences. The results showed that participants biased their heading estimates towards the straight-ahead direction and previously seen headings, which increased with the growing experience conflict. This suggests that both long- and short-term experiences simultaneously affect visual perception. Finally, we developed two Bayesian models (Model 1 vs. Model 2) based on two assumptions that the experience conflict altered the likelihood distribution of sensory representation or the motor response system. The results showed that both models accurately predicted participants' estimation biases. However, Model 1 predicted a higher variance of serial dependence compared to Model 2, while Model 2 predicted a higher variance of the bias towards the straight-ahead direction compared to Model 1. This suggests that the experience conflict can influence visual perception by affecting both sensory and motor response systems. Taken together, the current study systematically revealed the effects of long- and short-term experiences on visual perception and the underlying Bayesian processing mechanisms.

Perceptual Thresholds for Radial Optic Flow Distortion in Near-Eye Stereoscopic Displays.

We provide the first perceptual quantification of user's sensitivity to radial optic flow artifacts and demonstrate a promising approach for masking this optic flow artifact via blink suppression. Near-eye HMOs allow users to feel immersed in virtual environments by providing visual cues, like motion parallax and stereoscopy, that mimic how we view the physical world. However, these systems exhibit a variety of perceptual artifacts that can limit their usability and the user's sense of presence in VR. One well-known artifact is the vergence-accommodation conflict (VAC). Varifocal displays can mitigate VAC, but bring with them other artifacts such as a change in virtual image size (radial optic flow) when the focal plane changes. We conducted a set of psychophysical studies to measure users' ability to perceive this radial flow artifact before, during, and after self-initiated blinks. Our results showed that visual sensitivity was reduced by a factor of 10 at the start and for ~70 ms after a blink was detected. Pre- and post-blink sensitivity was, on average, ~O.15% image size change during normal viewing and increased to ~1.5- 2.0% during blinks. Our results imply that a rapid (under 70 ms) radial optic flow distortion can go unnoticed during a blink. Furthermore, our results provide empirical data that can be used to inform engineering requirements for both hardware design and software-based graphical correction algorithms for future varifocal near-eye displays. Our project website is available at https://gamma.umd.edu/ROF/.

Adaptive river flow measurement method based on spatiotemporal image velocimetry and optical flow.

This paper proposes an adaptive river discharge measurement method based on spatiotemporal image velocimetry (STIV) and optical flow to solve the problem of blurred texture features and limited measurement accuracy under complex natural environmental conditions. Optical flow tracking generates spatiotemporal images by following the flow mainstream direction of rivers with both regular and irregular natural banks. A texture similarity function filtering method effectively enhances spatiotemporal texture features. The proposed method is applied to a natural river, with measurement results from a propeller-type current meter used as truth values. It is evaluated and compared with three other methods regarding measurement accuracy, error, and other evaluation indices. The results demonstrate that the method significantly improves spatiotemporal image quality. Its estimation outcomes perform better across all evaluation metrics, enhancing the adaptability and accuracy of the flow measurement method.

Postural stability assessment in expert versus amateur basketball players during optic flow stimulation.

We evaluated the role of visual stimulation on postural muscles and the changes in the center of pressure (CoP) during standing posture in expert and amateur basketball players. Participants were instructed to look at a fixation point presented on a screen during foveal, peripheral, and full field optic flow stimuli. Postural mechanisms and motor strategies were assessed by simultaneous recordings of stabilometric, oculomotor, and electromyographic data during visual stimulation. We found significant differences between experts and amateurs in the orientation of visual attention. Experts oriented attention to the right of their visual field, while amateurs to the bottom-right. The displacement in the CoP mediolateral direction showed that experts had a greater postural sway of the right leg, while amateurs on the left leg. The entropy-based data analysis of the CoP mediolateral direction exhibited a greater value in amateurs than in experts. The root-mean-square and the coactivation index analysis showed that experts activated mainly the right leg while amateurs the left leg. In conclusion, playing sports for years seems to have induced some strong differences in the standing posture between the right and left sides. Even during non-ecological visual stimulation, athletes maintain postural adaptations to counteract the body oscillation.

Conv3D-Based Video Violence Detection Network Using Optical Flow and RGB Data.

Detecting violent behavior in videos to ensure public safety and security poses a significant challenge. Precisely identifying and categorizing instances of violence in real-life closed-circuit television, which vary across specifications and locations, requires comprehensive understanding and processing of the sequential information embedded in these videos. This study aims to introduce a model that adeptly grasps the spatiotemporal context of videos within diverse settings and specifications of violent scenarios. We propose a method to accurately capture spatiotemporal features linked to violent behaviors using optical flow and RGB data. The approach leverages a Conv3D-based ResNet-3D model as the foundational network, capable of handling high-dimensional video data. The efficiency and accuracy of violence detection are enhanced by integrating an attention mechanism, which assigns greater weight to the most crucial frames within the RGB and optical-flow sequences during instances of violence. Our model was evaluated on the UBI-Fight, Hockey, Crowd, and Movie-Fights datasets; the proposed method outperformed existing state-of-the-art techniques, achieving area under the curve scores of 95.4, 98.1, 94.5, and 100.0 on the respective datasets. Moreover, this research not only has the potential to be applied in real-time surveillance systems but also promises to contribute to a broader spectrum of research in video analysis and understanding.

Real needle for minimal invasive procedures training using motion sensors and optical flow.

Minimally invasive percutaneous insertion procedures are widely used techniques in medicine. Their success is highly dependent on the skills of the practitioner. This paper presents a haptic simulator for training in these procedures, whose key component is a real percutaneous insertion needle with a sensory system incorporated to track its 3D location at every instant. By means of the proposed embedded vision system, the attitude (spatial orientation) and depth of insertion of a real needle are estimated. The proposal is founded on a novel depth estimation procedure based on optical flow techniques, complemented by sensory fusion techniques with the attitude calculated with data from an Inertial Measurement Unit (IMU) sensor. This procedure allows estimating the needle attitude with an accuracy of tenths of a degree and the displacement with an accuracy of millimeters. The computational algorithm runs on an embedded computer with real-time constraints for tracking the movement of a real needle. This haptic needle location data is used to reproduce the movement of a virtual needle within a simulation app. As a fundamental result, an ergonomic and realistic training simulator has been successfully constructed for healthcare professionals to acquire the mental model and motor skills necessary to practice percutaneous procedures successfully.

OIF-Net: An Optical Flow Registration-Based PET/MR Cross-Modal Interactive Fusion Network for Low-Count Brain PET Image Denoising.

The short frames of low-count positron emission tomography (PET) images generally cause high levels of statistical noise. Thus, improving the quality of low-count images by using image postprocessing algorithms to achieve better clinical diagnoses has attracted widespread attention in the medical imaging community. Most existing deep learning-based low-count PET image enhancement methods have achieved satisfying results, however, few of them focus on denoising low-count PET images with the magnetic resonance (MR) image modality as guidance. The prior context features contained in MR images can provide abundant and complementary information for single low-count PET image denoising, especially in ultralow-count (2.5%) cases. To this end, we propose a novel two-stream dual PET/MR cross-modal interactive fusion network with an optical flow pre-alignment module, namely, OIF-Net. Specifically, the learnable optical flow registration module enables the spatial manipulation of MR imaging inputs within the network without any extra training supervision. Registered MR images fundamentally solve the problem of feature misalignment in the multimodal fusion stage, which greatly benefits the subsequent denoising process. In addition, we design a spatial-channel feature enhancement module (SC-FEM) that considers the interactive impacts of multiple modalities and provides additional information flexibility in both the spatial and channel dimensions. Furthermore, instead of simply concatenating two extracted features from these two modalities as an intermediate fusion method, the proposed cross-modal feature fusion module (CM-FFM) adopts cross-attention at multiple feature levels and greatly improves the two modalities' feature fusion procedure. Extensive experimental assessments conducted on real clinical datasets, as well as an independent clinical testing dataset, demonstrate that the proposed OIF-Net outperforms the state-of-the-art methods.

The Effects of Optical Flow Perturbations on Standing Balance in People With Multiple Sclerosis.

Multiple sclerosis is a neurodegenerative disease that causes balance deficits, even in early stages. Evidence suggests that people with multiple sclerosis (PwMS) rely more on vision to maintain balance, and challenging balance with optical flow perturbations may be a practical screening for balance deficits. Whether these perturbations affect standing balance in PwMS is unknown. Therefore, the purpose of this study was to examine how optical flow perturbations affect standing balance in PwMS. We hypothesized that perturbations would cause higher variability in PwMS compared with matched controls during standing and that standing balance would be more susceptible to anterior-posterior (A-P) perturbations than medial-lateral (M-L) perturbations. Thirteen PwMS and 13 controls stood under 3 conditions: unperturbed, M-L perturbation, and A-P perturbations. A-P perturbations caused significantly higher A-P trunk sway variability in PwMS than controls, although both groups had similar center-of-pressure variability. Both perturbations increased variability in A-P trunk sway and center of pressure. Trunk variability data supported the hypothesis that PwMS were more susceptible to optical flow perturbations than controls. However, the hypothesis that A-P perturbations would affect balance more than M-L perturbations was partially supported. These results suggest potential for optical flow perturbations to identify balance deficits in PwMS.

Optical Flow-Guided Cine MRI Segmentation With Learned Corrections.

In cardiac cine magnetic resonance imaging (MRI), the heart is repeatedly imaged at numerous time points during the cardiac cycle. Frequently, the temporal evolution of a certain region of interest such as the ventricles or the atria is highly relevant for clinical diagnosis. In this paper, we devise a novel approach that allows for an automatized propagation of an arbitrary region of interest (ROI) along the cardiac cycle from respective annotated ROIs provided by medical experts at two different points in time, most frequently at the end-systolic (ES) and the end-diastolic (ED) cardiac phases. At its core, a 3D TV- L1 -based optical flow algorithm computes the apparent motion of consecutive MRI images in forward and backward directions. Subsequently, the given terminal annotated masks are propagated by this bidirectional optical flow in 3D, which results, however, in improper initial estimates of the segmentation masks due to numerical inaccuracies. These initially propagated segmentation masks are then refined by a 3D U-Net-based convolutional neural network (CNN), which was trained to enforce consistency with the forward and backward warped masks using a novel loss function. Moreover, a penalization term in the loss function controls large deviations from the initial segmentation masks. This method is benchmarked both on a new dataset with annotated single ventricles containing patients with severe heart diseases and on a publicly available dataset with different annotated ROIs. We emphasize that our novel loss function enables fine-tuning the CNN on a single patient, thereby yielding state-of-the-art results along the complete cardiac cycle.

Optical Flow Analysis of Paralaryngeal Muscle Movement.

OBJECTIVES: The paralaryngeal muscles are thought to be hyperfunctional with phonation in patients with primary muscle tension dysphonia (pMTD). However, objective, quantitative tools to assess paralaryngeal movement patterns lack. The objectives of this study were to (1) validate the use of optical flow to characterize paralaryngeal movement patterns with phonation, (2) characterize phonatory optical flow velocities and variability of the paralaryngeal muscles before and after a vocal load challenge, and (3) compare phonatory optical flow measures to standard laryngoscopic, acoustic, and self-perceptual assessments. METHODS: Phonatory movement velocities and variability of the paralaryngeal muscles at vocal onsets and offsets were quantified from ultrasound videos and optical flow methods across 42 subjects with and without a diagnosis of pMTD, before and after a vocal load challenge. Severity of laryngoscopic mediolateral supraglottic compression, acoustic perturbation, and ratings of vocal effort and discomfort were also obtained at both time points. RESULTS: There were no significant differences in optical flow measures of the paralaryngeal muscles with phonation between patients with pMTD and controls. Patients with pMTD had significantly more supraglottic compression, higher acoustic perturbations, and higher vocal effort and vocal tract discomfort ratings. Vocal load had a significant effect on vocal effort and discomfort but not on supraglottic compression, acoustics, or optical flow measures of the paralaryngeal muscles. CONCLUSION: Optical flow methods can be used to study paralaryngeal muscle movement velocity and variability patterns during vocal productions, although the role of the paralaryngeal in pMTD diagnostics (e.g., vocal hyperfunction) remains suspect. LEVEL OF EVIDENCE: 2 Laryngoscope, 134:1792-1801, 2024.

Does the effect of walking balance perturbations generalize across contexts?

Balance perturbations are used to study locomotor instability. However, these perturbations are designed to provoke a specific context of instability that may or may not generalize to a broader understanding of falls risk. The purpose of this study was to determine if the effect of balance perturbations on instability generalizes across contexts. 29 younger adults and 28 older adults completed four experimental trials, including unperturbed walking and walking while responding to three perturbation contexts: mediolateral optical flow, treadmill-induced slips, and lateral waist-pulls. We quantified the effect of perturbations as an absolute change in margin of stability from unperturbed walking. We found significant changes in mediolateral and anteroposterior margin of stability for all perturbations compared to unperturbed walking in both cohorts (p-values ≤ 0.042). In older adults, the mediolateral effects of lateral waist-pulls significantly correlated with those of optical flow perturbations and treadmill-induced slips (r ≥ 0.398, p-values ≤ 0.036). In younger adults but not in older adults, we found positive and significant correlations between the anteroposterior effect of waist-pull perturbations and optical flow perturbations, and the anteroposterior and mediolateral effect of treadmill-induced slips (r ≥ 0.428, p-values ≤ 0.021). We found no "goldilocks" perturbation paradigm to endorse that would support universal interpretations about locomotor instability. Building the most accurate patient profiles of instability likely requires a series of perturbation paradigms designed to emulate the variety of environmental contexts in which falls may occur.

Interaction of contour geometry and optic flow in determining relative depth of surfaces.

Dynamic occlusion, such as the accretion and deletion of texture near a boundary, is a major factor in determining relative depth of surfaces. However, the shape of the contour bounding the dynamic texture can significantly influence what kind of 3D shape, and what relative depth, are conveyed by the optic flow. This can lead to percepts that are inconsistent with traditional accounts of shape and depth from motion, where accreting/deleting texture can indicate the figural region, and/or 3D rotation can be perceived despite the constant speed of the optic flow. This suggests that the speed profile of the dynamic texture and the shape of its bounding contours combine to determine relative depth in a way that is not explained by existing models. Here, we investigated how traditional structure-from-motion principles and contour geometry interact to determine the relative-depth interpretation of dynamic textures. We manipulated the consistency of the dynamic texture with rotational or translational motion by varying the speed profile of the texture. In Experiment 1, we used a multi-region figure-ground display consisting of regions with dots moving horizontally in opposite directions in adjacent regions. In Experiment 2, we used stimuli including two regions separated by a common border, with dot textures moving horizontally in opposite directions. Both contour geometry (convexity) and the speed profile of the dynamic dot texture influenced relative-depth judgments, but contour geometry was the stronger factor. The results underscore the importance of contour geometry, which most current models disregard, in determining depth from motion.

Joint estimation of pose, depth, and optical flow with a competition-cooperation transformer network.

Estimating depth, ego-motion, and optical flow from consecutive frames is a critical task in robot navigation and has received significant attention in recent years. In this study, we propose PDF-Former, an unsupervised joint estimation network comprising a full transformer-based framework, as well as a competition and cooperation mechanism. The transformer framework captures global feature dependencies and is customized for different task types, thereby improving the performance of sequential tasks. The competition and cooperation mechanisms enable the network to obtain additional supervisory information at different training stages. Specifically, the competition mechanism is implemented early in training to achieve iterative optimization of 6 DOF poses (rotation and translation information from the target image to the two reference images), the depth of target image, and optical flow (from the target image to the two reference images) estimation in a competitive manner. In contrast, the cooperation mechanism is implemented later in training to facilitate the transmission of results among the three networks and mutually optimize the estimation results. We conducted experiments on the KITTI dataset, and the results indicate that PDF-Former has significant potential to enhance the accuracy and robustness of sequential tasks in robot navigation.

Pig Movement Estimation by Integrating Optical Flow with a Multi-Object Tracking Model.

Pig husbandry constitutes a significant segment within the broader framework of livestock farming, with porcine well-being emerging as a paramount concern due to its direct implications on pig breeding and production. An easily observable proxy for assessing the health of pigs lies in their daily patterns of movement. The daily movement patterns of pigs can be used as an indicator of their health, in which more active pigs are usually healthier than those who are not active, providing farmers with knowledge of identifying pigs' health state before they become sick or their condition becomes life-threatening. However, the conventional means of estimating pig mobility largely rely on manual observations by farmers, which is impractical in the context of contemporary centralized and extensive pig farming operations. In response to these challenges, multi-object tracking and pig behavior methods are adopted to monitor pig health and welfare closely. Regrettably, these existing methods frequently fall short of providing precise and quantified measurements of movement distance, thereby yielding a rudimentary metric for assessing pig health. This paper proposes a novel approach that integrates optical flow and a multi-object tracking algorithm to more accurately gauge pig movement based on both qualitative and quantitative analyses of the shortcomings of solely relying on tracking algorithms. The optical flow records accurate movement between two consecutive frames and the multi-object tracking algorithm offers individual tracks for each pig. By combining optical flow and the tracking algorithm, our approach can accurately estimate each pig's movement. Moreover, the incorporation of optical flow affords the capacity to discern partial movements, such as instances where only the pig's head is in motion while the remainder of its body remains stationary. The experimental results show that the proposed method has superiority over the method of solely using tracking results, i.e., bounding boxes. The reason is that the movement calculated based on bounding boxes is easily affected by the size fluctuation while the optical flow data can avoid these drawbacks and even provide more fine-grained motion information. The virtues inherent in the proposed method culminate in the provision of more accurate and comprehensive information, thus enhancing the efficacy of decision-making and management processes within the realm of pig farming.

Optical Flow Glottovibrogram for the examination of vocal fold pathology.

Laryngeal high-speed video endoscopy is performed to examine the cycles of vocal fold vibrations in detail and to diagnose voice abnormalities. One of the recent image processing techniques for visualizing vocal fold vibration is optical flow-based playbacks, which include optical flow kymograms (OFKG) for local dynamics, optical flow glottovibrogram (OFGVG) and glottal optical flow waveforms (GOFW) for global dynamics. In recent times, various optical flow computing algorithms have been developed. In this paper, we used four well-known optical flow algorithms Horn Schunk, Lucas Kanade, Gunnar Farneback, and TVL1 to construct the optical flow playbacks. The proposed playback reliability is examined by comparing them to traditional representations such as Phonovibrogram (PVG). Since PVG and OFGVG are interconnected, a comparison study was carried out to better comprehend their interaction.Clinical Relevance- Both OFGVG and PVG add to the precision of interpreting pathological conditions by offering complementary information to the conventional spatiotemporal representations.

Illusory percepts of curvilinear self-motion when moving through crowds.

Self-motion generates optic flow, a pattern of expanding visual motion. Heading estimation from optic flow analysis is accurate in rigid environments, but it becomes challenging when other human walkers introduce independent motion to the scene. Previous studies showed that heading perception is surprisingly accurate when moving through a crowd of walkers but revealed strong heading biases when either articulation or translation of biological motion were presented in isolation. We hypothesized that these biases resulted from misperceiving the self-motion as curvilinear. Such errors might manifest as opposite biases depending on whether the observer perceived the crowd motion as indication of his/her self-translation or self-rotation. Our study investigated the link between heading biases and illusory path perception. Participants assessed heading and path perception while observing optic flow stimuli with varying walker movements. Self-motion perception was accurate during natural locomotion (articulation and translation), but significant heading biases occurred when walkers only articulated or translated. In this case, participants often reported a curved path of travel. Heading error and curvature pointed in opposite directions. On average, participants perceived the walker motion as evidence for viewpoint rotation leading to curvilinear path percepts.

Neural mechanisms to incorporate visual counterevidence in self-movement estimation.

In selecting appropriate behaviors, animals should weigh sensory evidence both for and against specific beliefs about the world. For instance, animals measure optic flow to estimate and control their own rotation. However, existing models of flow detection can be spuriously triggered by visual motion created by objects moving in the world. Here, we show that stationary patterns on the retina, which constitute evidence against observer rotation, suppress inappropriate stabilizing rotational behavior in the fruit fly Drosophila. In silico experiments show that artificial neural networks (ANNs) that are optimized to distinguish observer movement from external object motion similarly detect stationarity and incorporate negative evidence. Employing neural measurements and genetic manipulations, we identified components of the circuitry for stationary pattern detection, which runs parallel to the fly's local motion and optic-flow detectors. Our results show how the fly brain incorporates negative evidence to improve heading stability, exemplifying how a compact brain exploits geometrical constraints of the visual world.

Generalizing the optic flow equalization control law to an asymmetrical person-plus-object system.

Visually guided action in humans occurs in part through the use of control laws, which are dynamical equations in which optical information modulates an actor's interaction with their environment. For example, humans locomote through the center of a corridor by equalizing the speed of optic flow across their left and right fields of view. This optic flow equalization control law relies on a crucial assumption: that the shape of the body relative to the eyes is laterally symmetrical. Humans engaging in tool use are often producing person-plus-object systems that are not laterally symmetrical, such as when they hold a tool, bag, or briefcase in one hand, or when they drive a vehicle. This experiment tests a new generalized control law for centered steering that accounts for asymmetries produced by external tool use. Participants held an asymmetrical bar and centered themselves within a virtual moving hallway while the speed of the virtual walls were systematically changed. The results demonstrate that humans engaging with an asymmetrical tool can (1) perceive the asymmetry of a person-plus-object system, (2) use that information to modulate the use of optic flow equalization control laws for centered steering, and (3) functionally incorporate the asymmetrical tool into their perception-action system to successfully navigate their environment.

Effect of expansive optic flow and lateral motion parallax on depth estimation with normal and artificially reduced acuity.

When an observer moves in space, the retinal projection of a stationary object either expands if the motion is toward the object or shifts horizontally if the motion contains a lateral component. This study examined the impact of expansive optic flow and lateral motion parallax on the accuracy of depth perception for observers with normal or artificially reduced acuity and asked whether any benefit is due to the continuous motion or to the discrete object image displacement. Stationary participants viewed a virtual room on a computer screen. They used an on-screen slider to estimate the depth of a target object relative to a reference object after seeing 2-second videos simulating five conditions: static viewing, expansive optic flow, and lateral motion parallax in either continuous motion or image displacement. Ten participants viewed the stimuli with normal acuity in Experiment 1 and 11 with three levels of artificially reduced acuity in Experiment 2. Linear regression models represented the relationship between the depth estimates of participants and the ground truth. Lateral motion parallax produced more accurate depth estimates than expansive optic flow and static viewing. Depth perception with continuous motion was more accurate than that with displacement under mild and moderate, but not severe, acuity reduction. For observers with both normal and artificially reduced acuity, lateral motion parallax was more helpful for object depth estimation than expansive optic flow, and continuous motion parallax was more helpful than object image displacement.

Virtual reality-enhanced walking in people post-stroke: effect of optic flow speed and level of immersion on the gait biomechanics.

BACKGROUND: Optic flow-the apparent visual motion experienced while moving-is absent during treadmill walking. With virtual reality (VR), optic flow can be controlled to mediate alterations in human walking. The aim of this study was to investigate (1) the effects of fully immersive VR and optic flow speed manipulation on gait biomechanics, simulator sickness, and enjoyment in people post-stroke and healthy people, and (2) the effects of the level of immersion on optic flow speed and sense of presence. METHODS: Sixteen people post-stroke and 16 healthy controls performed two VR-enhanced treadmill walking sessions: the semi-immersive GRAIL session and fully immersive head-mounted display (HMD) session. Both consisted of five walking trials. After two habituation trials (without and with VR), participants walked three more trials under the following conditions: matched, slow, and fast optic flow. Primary outcome measures were spatiotemporal parameters and lower limb kinematics. Secondary outcomes (simulator sickness, enjoyment, and sense of presence) were assessed with the Simulator Sickness Questionnaire, Visual Analogue Scales, and Igroup Presence Questionnaire. RESULTS: When walking with the immersive HMD, the stroke group walked with a significantly slower cadence (-3.69strides/min, p = 0.006), longer stride time (+ 0.10 s, p = 0.017) and stance time for the unaffected leg (+ 1.47%, p = 0.001) and reduced swing time for the unaffected leg (- 1.47%, p = 0.001). Both groups responded to the optic flow speed manipulation such that people accelerated with a slow optic flow and decelerated with a fast optic flow. Compared to the semi-immersive GRAIL session, manipulating the optic flow speed with the fully immersive HMD had a greater effect on gait biomechanics whilst also eliciting a higher sense of presence. CONCLUSION: Adding fully immersive VR while walking on a self-paced treadmill led to a more cautious gait pattern in people post-stroke. However, walking with the HMD was well tolerated and enjoyable. People post-stroke altered their gait parameters when optic flow speed was manipulated and showed greater alterations with the fully-immersive HMD. Further work is needed to determine the most effective type of optic flow speed manipulation as well as which other principles need to be implemented to positively influence the gait pattern of people post-stroke. TRIAL REGISTRATION NUMBER: The study was pre-registered at ClinicalTrials.gov (NCT04521829).