Objective: The King–Devick (KD) test, which is based on rapid number naming speed, is a performance measure that adds vision and eye movement assessments to sideline concussion testing. We performed a laboratory‐based study to characterize ocular motor behavior during the KD test in a patient cohort with chronic concussion to identify features associated with prolonged KD reading times.Methods: Twenty‐five patients with a concussion history (mean age: 31) were compared to control participants with no concussion history (n = 42, mean age: 32). Participants performed a computerized KD test under infrared‐based video‐oculography.Results: Average intersaccadic intervals for task‐specific saccades were significantly longer among concussed patients compared to controls (324.4 ± 85.6 msec vs. 286.1 ± 49.7 msec, P = 0.027). Digitized KD reading times were prolonged in concussed participants versus controls (53.43 ± 14.04 sec vs. 43.80 ± 8.55 sec, P = 0.004) and were highly correlated with intersaccadic intervals. Concussion was also associated with a greater number of saccades during number reading and larger average deviations of saccade endpoint distances from the centers of the to‐be‐read numbers (1.22 ± 0.29° vs. 0.98 ± 0.27°, P = 0.002). There were no differences in saccade peak velocity, duration, or amplitude.Interpretation: Prolonged intersaccadic intervals, greater numbers of saccades, and larger deviations of saccade endpoints underlie prolonged KD reading times in chronic concussion. The KD test relies upon a diffuse neurocognitive network that mediates the fine control of efferent visual function. One sequela of chronic concussion may be disruption of this system, which may produce deficits in spatial target selection and planning of eye movements.

Acute and chronic disease processes that lead to cerebral injury can often be clinically challenging diagnostically, prognostically, and therapeutically. Neurodegenerative processes are one such elusive diagnostic group, given their often diffuse and indolent nature, creating difficulties in pinpointing specific structural abnormalities that relate to functional limitations. A number of studies in recent years have focused on eye-hand coordination in the setting of acquired brain injury (ABI), highlighting the important set of interconnected functions of the eye and hand and their relevance in neurological conditions. These experiments, which have concentrated on focal lesion-based models, have significantly improved our understanding of neurophysiology and underscored the sensitivity of biomarkers in acute and chronic neurological disease processes, especially when such biomarkers are combined synergistically. To better understand eye-hand coordination and its connection with ABI, there is a need to clarify its definition and to delineate its neuroanatomical and computational underpinnings. Successful eye-hand coordination relies on the complex feedback- and prediction-mediated relationship between the visual, ocular-motor, and manual motor systems and takes advantage of finely orchestrated synergies between these systems in both the spatial and temporal domains. Interactions of this type are representative of functional sensory-motor control, and whose disruption constitutes one of the most frequent deficits secondary to brain injury. The present review describes the visually mediated planning and control of eye movements, hand movements, and their coordination, with a particular focus on deficits that occur following neurovascular, neurotraumatic, and neurodegenerative conditions. Following this review, we also discuss potential future research directions, highlighting objective eye-hand coordination as a sensitive biomarker complement within acute and chronic neurological disease processes.

People with blindness and low vision (pBLV) encounter substantial challenges when it comes to comprehensive scene recognition and precise object identification in unfamiliar environments. Additionally, due to the vision loss, pBLV have difficulty in accessing and identifying potential tripping hazards independently. Previous assistive technologies for the visually impaired often struggle in real-world scenarios due to the need for constant training and lack of robustness, which limits their effectiveness, especially in dynamic and unfamiliar environments, where accurate and efficient perception is crucial. Therefore, we frame our research question in this paper as: How can we assist pBLV in recognizing scenes, identifying objects, and detecting potential tripping hazards in unfamiliar environments, where existing assistive technologies often falter due to their lack of robustness? We hypothesize that by leveraging large pretrained foundation models and prompt engineering, we can create a system that effectively addresses the challenges faced by pBLV in unfamiliar environments. Motivated by the prevalence of large pretrained foundation models, particularly in assistive robotics applications, due to their accurate perception and robust contextual understanding in real-world scenarios induced by extensive pretraining, we present a pioneering approach that leverages foundation models to enhance visual perception for pBLV, offering detailed and comprehensive descriptions of the surrounding environment and providing warnings about potential risks. Specifically, our method begins by leveraging a large-image tagging model (i.e., Recognize Anything Model (RAM)) to identify all common objects present in the captured images. The recognition results and user query are then integrated into a prompt, tailored specifically for pBLV, using prompt engineering. By combining the prompt and input image, a vision-language foundation model (i.e., InstructBLIP) generates detailed and comprehensive descriptions of the environment and identifies potential risks in the environment by analyzing environmental objects and scenic landmarks, relevant to the prompt. We evaluate our approach through experiments conducted on both indoor and outdoor datasets. Our results demonstrate that our method can recognize objects accurately and provide insightful descriptions and analysis of the environment for pBLV.