Faculty Bibliography

BACKGROUND:Long-term effect of COVID-19 (Long COVID) may persist for months or years after SARS-CoV-2 infection, but longer-term cardiopulmonary manifestations have not been previously reported. OBJECTIVES/OBJECTIVE:The objective of the study was to characterize cardiopulmonary function after SARS-CoV-2 infection in a digital health substudy of the nationwide Researching COVID-19 to Enhance Recovery Adult Cohort Study. METHODS:Associations between wearable sensor device measures of cardiopulmonary fitness and survey-derived Long COVID symptoms were estimated over a 6-month window at least 6 months after infection using linear regression models adjusted for wear time, age, sex, race/ethnicity, and body mass index. RESULTS:Among 1,475 participants (72% female, 65% non-Hispanic White) a median of 21 months (IQR: 15-31 months) after infection, 498 (34%) had high symptom burden as characterized by the Researching COVID-19 to Enhance Recovery Long COVID Research Index (LCRI). High LCRI (vs low LCRI) was associated with significantly lower heart rate variability (-4.4 ms; 95% CI: -6.5 to -2.4; P < 0.001), higher resting heart rate (+1.5 beats/min [+0.7 to +2.4]; P < 0.001), fewer metabolic equivalent of task minutes (-96.3 [-128.8 to -63.8]; P < 0.001), lower step counts (-1,624 steps/day [-1,952 to -1,296]; P < 0.001), and lower activity levels (-7.9 minutes/day very or fairly active [-10.9 to -5.0]; P < 0.001). Hierarchal clustering analysis identified two subphenotypes with abnormal cardiovascular measures associated with low quality of life scores. CONCLUSIONS:Long COVID is associated with worse cardiovascular fitness. Additional studies are needed to determine if Long COVID is a novel risk factor for incident cardiovascular disease.

Precise quantification of eye-hand coordination (EHC) during pediatric dexterity tasks is limited by the lack of practical, high-resolution hand tracking methods suitable for children with brain injury or neurodegenerative disease. Traditional marker-based motion capture systems and instrumented gloves can interfere with natural grasp patterns and are often difficult to implement in clinical pediatric settings. We describe an adaptation of the Anipose markerless 3D pose estimation framework to enable synchronized three-dimensional hand kinematics and eye tracking during the Nine-Hole Peg Test (9HPT). The method integrates multi-camera video acquisition with task-specific neural network training optimized to detect fine finger movements across diverse pediatric hand sizes and grasp configurations. Camera placement and recording geometry were configured to reduce occlusion during peg manipulation and maintain multi-view visibility of hand landmarks. Model validation demonstrated low pixel error and stable three-dimensional reconstruction following confidence-based thresholding. The resulting workflow generates synchronized 2D and 3D visualizations, spatial coordinate outputs, reprojection-error metrics, and landmark confidence scores without requiring wearable sensors. This approach broadens the applicability of eye-hand coordination research within pediatric clinical populations and facilitates the development of more precise, quantitatively informed diagnostic assessments and targeted neurorehabilitation strategies for children with neurologic injury. • Markerless multi-camera 3D reconstruction of pediatric hand kinematics during the 9HPT • Integration of synchronized eye tracking and task-specific neural network training • Output of validated 3D coordinates, confidence metrics, and visualization files suitable for clinical research.

PURPOSE/UNASSIGNED:Optical character recognition (OCR), a process that converts printed or handwritten text into machine-readable form, is widely used in assistive technology for people with blindness and low vision. Yet most evaluations rely on static datasets that do not reflect the challenges of mobile use. This study evaluated how OCR performance changes under static and walking conditions relevant to real-world navigation. METHODS/UNASSIGNED:Static tests varied distance from 1-7 metres and viewing angle from 0°-75°. Dynamic tests examined the impact of motion by varying walking speed from 0.8 m/s to 1.8 m/s and compared head-mounted, shoulder-mounted, and handheld positions. We evaluated a smartphone and smart glasses, including the phone's main and ultra-wide cameras, across four OCR engines: Google Vision, PaddleOCR 3.0, EasyOCR, and Tesseract. Dynamic tests used PaddleOCR 3.0. Accuracy was computed at the character level using the Levenshtein ratio against manually defined ground truth. RESULTS/UNASSIGNED:Recognition accuracy declined with increased walking speed and wider viewing angles. Google Vision achieved the highest overall accuracy, with PaddleOCR close behind as the strongest open-source alternative. Across devices, the phone's main camera achieved the highest accuracy, and a shoulder-mounted placement yielded the highest average among body positions; however, differences among shoulder, head, and hand were not statistically significant. CONCLUSION/UNASSIGNED:OCR performance depends on the recognition engine, camera hardware, field of view, device placement, and user motion. OCR systems for navigation should be evaluated under dynamic, mobility-relevant conditions rather than static images alone and designed to balance coverage, recognition accuracy, and practical deployment.

Historically hindered by a lack of access to academic, political, financial, technological, scientific, and social resources, most people living with disability have been unable to successfully merge their lived experience with the traditional research process. The lack of this community's perspective has been an ongoing missed opportunity for the broadening and relevance of research around disability. Patient-scientists, however, bridge the gap. They are individuals who act as patient research partners (PRPs) with the valuable addition of a research and/or medical degree. Their embodied expertise, combined with their academic accreditation, seamlessly positions them to work within the academic system. With a foot in both worlds, they are equipped to generate real change for themselves and others living with their condition. Patients are encouraged to participate in their own clinical care, although PRPs remain relatively uncommon. Even more scarce are patient-scientists, who serve as intellectual peers with expertise in technical and experiential domains. Their research training gives them an invaluable role: to act as both scientist and patient at once. This special communication builds on ongoing efforts to bolster patient participation in rehabilitation research by focusing on patient-scientists and highlighting their potential to enhance rehabilitation research processes.

BACKGROUND:Individuals with sensory processing disorders (SPDs) face challenges in wayfinding due to heightened sensitivity to environmental stimuli like noise and lighting. Although sensory maps have aided SPD individuals by aligning navigational routes with sensory needs, standardized protocols for creating these maps are lacking, with current methods largely qualitative. OBJECTIVE:This study aimed to establish a standardized, quantitative protocol for sensory map creation, evaluating sound, brightness, and crowding density in a healthcare setting to enhance map precision and support future automation. METHODS:Sensory data were collected from the 1st and 17th floors of a large urban ambulatory care center in New York City. A qualitative spatial audit was initially conducted, followed by quantitative measurements of sound (decibels), brightness (lux), and crowding density at designated nodes. Using Python, we developed Voronoi diagrams to visualize intensity distributions across floorplans, applying statistical methods to ensure data accuracy and consistency. RESULTS:Qualitative assessments identified high-stimulus areas, particularly in the main lobby and elevator zones, which aligned closely with quantitative findings. Brightness peaked in central, naturally lit areas, while noise levels were highest near heating, ventilation, and air conditioning (HVAC) systems and entry points. The quantitative method enabled a more nuanced representation, enhancing map detail and reliability. CONCLUSIONS:The developed quantitative protocol offers a robust framework for sensory mapping, improving accessibility for individuals with SPDs in complex spaces. This approach holds potential for automation, addressing current reproducibility limitations and advancing inclusive design in public and healthcare settings.

The Global Navigation Satellite System (GNSS) is commonly used for outdoor positioning. However, its effectiveness diminishes in urban canyons and indoor environments attributed to signal blockage. This study aims to explore the potential of GNSS signals penetrating indoor spaces through windows and to enhance indoor positioning with Three-Dimensional Mapping-Aided (3DMA) GNSS, a concept generally applied outdoors. The research employs a 3D model of a corridor with manually labeled window locations to predict satellite visibility within indoor areas. The study integrates Pedestrian Dead Reckoning (PDR) with an indoor Shadow-matching (I-SM) technique, utilizing an Extended Kalman Filter (EKF) to improve positioning accuracy. One of the findings indicates that the proposed method significantly enhances positioning performance and its availability, achieving a root mean square error (RMSE) that is 2 m better than using PDR alone or single epoch I-SM. The study concludes that integrating GNSS with I-SM technique and PDR can optimize an indoor positioning solution and highlights the potential for improved navigation solutions in complex urban environments.

PURPOSE/UNASSIGNED:Navigating urban environments poses significant challenges for individuals who are blind or have low vision, especially in areas affected by construction. Construction zones introduce hazards such as uneven surfaces, barriers, hazardous materials, excessive noise, and altered routes that obstruct familiar paths and compromise safety. Although navigation tools assist in trip planning, they often overlook these temporary obstacles. Existing hazard detection systems also struggle with the visual variability of construction sites. METHODS/UNASSIGNED:We developed a computer vision-based assistive system integrating three modules: an open-vocabulary object detector to identify diverse construction-related elements, a YOLO-based model specialised in detecting scaffolding and poles, and an optical character recognition module to interpret construction signage. RESULTS/UNASSIGNED:In static testing at seven construction sites using images from multiple stationary viewpoints, the system achieved 88.56% overall accuracy. It consistently identified relevant objects within 2-10 m and at approach angles up to 75°. At 2-4 m, detection was perfect (100%) across all angles. Even at 10 m, six of seven sites remained detectable within a 15° approach. In dynamic testing along a 0.5-mile urban route containing eight construction sites, the system analysed every frame of a first-person walking video. It achieved 87.26% accuracy in distinguishing construction from non-construction areas, rising to 92.0% with a 50-frame majority vote filter. CONCLUSION/UNASSIGNED:The system can reliably detect construction sites in real time and at sufficient distances to provide advance warnings, enabling individuals with visual impairments to make safer mobility decisions, such as proceeding with caution or rerouting.

BACKGROUND/UNASSIGNED:Outdoor navigation poses significant challenges for people with blindness or low vision, yet the role of gaze behaviour in supporting mobility remains underexplored. Fully sighted individuals typically adopt consistent scanning strategies, whereas those with visual impairments rely on heterogeneous adaptations shaped by residual vision and experience. METHODS/UNASSIGNED:We conducted a comparative eye-tracking study of fully sighted, low vision, blind, and fully blind participants navigating outdoor routes. Using a wearable eye tracker, we quantified fixation counts, fixation rate, fixation area, direction, peak fixation location, and walking speed. RESULTS/UNASSIGNED:Walking speed declined systematically with worsening vision. Fixation count increased with greater impairment, reflecting slower travel times and more frequent sampling. Fixation rate differed across groups, though between-group differences were generally not significant between most groups. Fixation spatial coverage decreased along the continuum of vision loss. Fixation patterns were most consistent in the fully sighted group. Peak fixation locations were centred in fully sighted participants but shifted outward and became more variable with impairment. CONCLUSION/UNASSIGNED:Gaze strategies during navigation form a graded continuum across vision groups, with fully sighted and fully blind participants at opposite poles and low vision and blind groups spanning the middle. Visual acuity alone does not predict functional gaze use, as rehabilitation experience and adaptive strategies strongly shape behaviour. These findings highlight the need for personalised rehabilitation and assistive technologies, with residual gaze patterns offering insight into mobility capacity and training opportunities for safer navigation.IMPLICATIONS FOR REHABILITATIONDistinct Residual Vision Patterns: This research reveals that residual vision patterns differ significantly, with fully sighted individuals exhibiting a consistent fixation pattern while low vision participants show more varied strategies during navigation.Highly Individualised Gaze Behaviours: Low vision participants demonstrate highly individualised gaze behaviours, indicating that a one-size-fits-all approach is inadequate for effective rehabilitation.Tailored Assistive Solutions: Assistive technologies and rehabilitation programs should be designed to address these unique, individualised needs, providing personalised feedback and training to enhance mobility and safety.

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