Ocular surface disorders such as Lid Wiper Epitheliopathy (LWE), Superior Epithelial Arcuate Lesion (SEAL), and contact lens-induced Limbal Stem Cell Deficiency (LSCD) as well as Superior Limbic Keratoconjunctivitis (SLK) affect one’s quality of life. Hence, it is imperative to investigate the underlying causes of these ocular surface disorders. During blink, the undersurface of the eyelid tends to interact with the cornea and the conjunctiva. The presence of a contact lens can add to the biomechanical frictional changes on these surfaces. To estimate these changes with and without a contact lens, a finite element model (FEM) of the eyelid wiper, eyeball, and contact lens was developed using COMSOL Multiphysics. Biomechanical properties such as von Mises stress (VMS) and displacement were calculated. Our study concluded that (a) maximum VMS was observed in the lid wiper in the absence of contact lens in the eye and (b) maximum VMS was observed in the superior 1.3 mm of the cornea in the presence of the contact lens in the eye. Thus, the development of friction-induced ocular surface disorders such as LWE, SLK, SEAL, and LSCD could be attributed to increased VMS. FEA is a useful simulation tool that helps us to understand the effect of blink on a normal eye with and without CL.

Tonometry, a procedure of glaucoma investigation is carried out effectively in all eye care clinics. The other testing method namely gonioscopy requires professional expertise and experience due to which optometrists/ophthalmologists avoid this test. Hence the proposed technology would let these two important glaucoma testing procedures be performed simultaneously with a common instrument “Tonogoniometry” and making detection easier, time-saving, and lowering uneasiness to the patient. Tonometry is implemented by embedding a vibration sensor on the 3-mirror goniolens. The experiment is performed on goat eyes and the data collected is statistically analyzed. The relation between the Intraocular pressure (IOP) of the eye and the sensor output is derived and is implemented in LabVIEW. During tonometry, the sensor output captured by the NI data acquisition card is fed to the LabVIEW platform which will display the evaluated IOP of the eye on the computer screen. Meanwhile, there is no obstruction to the light path through the goniolens to visualize the anterior chamber angle to perform gonioscopy. The experimental results epitomized that the rise in IOP of the eye will increase the intensity of vibration of the sensor and the sensor output will have a higher magnitude.

Stroke frequently leads to upper limb impairment, requiring precise evaluation to guide effective rehabilitation. Markerless motion-capture technologies have emerged as promising, noninvasive tools for objective and quantitative movement assessment. This comprehensive review summarizes advancements in markerless motion-capture systems for upper limb functional assessment in stroke rehabilitation and identifies current limitations and future opportunities. Relevant studies published between 2012 and 2025 were identified through PubMed, Scopus, IEEE Xplore, and Web of Science databases using search terms related to stroke, upper-limb motion, and markerless motion capture. Recent systems such as OpenPose, MediaPipe, and Theia3D demonstrate reliable kinematic accuracy (mean error < 6°) and feasibility for remote rehabilitation monitoring. However, variations in study protocols, small sample sizes, and limited clinical validation continue to hinder widespread clinical adoption. Markerless motion capture provides an accessible and objective approach for assessing upper-limb function after stroke. Future work should focus on standardizing validation procedures, improving real-time performance, and integrating these systems into routine clinical and telerehabilitation workflows.

As gesture-based contactless interfaces have gained prominence in healthcare, gaming, and industrial automation, performance degradation during rapid interaction remains a critical barrier to widespread adoption. Although existing research has focused predominantly on controlled virtual environments, the role of visual coordination cues in time-sensitive gesture-recognition scenarios remains largely unexplored. This study examined the impact of color-based visual coordination aids on user performance in rapid gesture-based tasks using Leap Motion technology. A game was designed where the participants interacted with the game setup using contactless hand tracking without electronic wearable sensors. Twelve participants were initially enrolled and began the study under two conditions: bare hands and sphere-color-matching gloves; however, two participants did not complete both conditions, and the final analysis included data from ten participants who completed the full protocol. Performance under the glove condition improved compared to bare hands. Accuracy increased from 80.33% to 84.33%, improving by 4 percentage points, and average error per session decreased by 75% from 2.40 to 0.60. Additionally, 70% of participants showed better performance accuracy. A paired t-test revealed statistically significant improvements with gloves, t(9) = 2.102, p = 0.035, α = 0.05, Cohen’s d_z = 0.665, 95% CI for d_z [0.082, 1.248]. Mixed-effects modeling confirmed that the intervention benefit generalized across both standard and accelerated task difficulty conditions (Gloves × Difficulty interaction: p = 0.473). These results show that a simple, low-cost visual coordination technique can significantly improve accuracy in time-sensitive human-computer interactions.

Gait analysis is simply a way of studying how a person walks to learn about their body’s movements and posture. Individuals with conditions that affect their ability to walk, such as cerebral palsy, stroke, or Parkinson’s disease, often undergo gait analysis in a specialized laboratory. The aspects of their walking that are measured determine their specific health issues and provide the information required by clinicians and healthcare providers. While gait tests offer valuable insights, inherent challenges exist for both patients and clinicians. Patients may experience discomfort, anxiety, physical limitations, and cost and accessibility barriers. Clinicians may experience differences in equipment, protocols, and reference values between laboratories, as well as interpretation difficulties. Traditional gait assessment solely depends on observations of things such as joint angles and the range of motion (ROM) with multiple cameras in a specialized room. As patients experience physical strain because of travel and test-related procedures, they may develop stress and anxiety, potentially leading to inaccurate readings. Patients often skip the hospital because they would have to take time off work. The availability of a portable device to measure gait may open the possibility of conducting analysis and rehabilitation in a home setting and can also be a screening tool for community hospitals, which can drastically improve patient recovery. In this work, a portable and easy-to-carry gait profiler was created to overcome the challenges faced by clinicians and patients with traditional gait analysis. The device can collect data in a variety of real-world settings, such as homes, clinics, and the community, offering an alternative to costly and inaccessible lab settings. The profiler precisely captures a patient’s natural gait under actual circumstances via computer vision. Low-light compatibility, which makes use of specialized cameras and infrared emitters to guarantee precise measurements even in poorly lit areas, is one of its primary features. The gait profiler created is a reliable and practical tool for clinical and research use, as it has been demonstrated to possess the same degree of accuracy, robustness, and usefulness as standard gait laboratory tests.