An open-source, FireWire camera-based, Labview-controlled image acquisition system for automated, dynamic pupillometry and blink detection

Pupil Size Prediction Techniques Based on Convolution Neural Network

The size of one’s pupil can indicate one’s physical condition and mental state. When we search related papers about AI and the pupil, most studies focused on eye-tracking. This paper proposes an algorithm that can calculate pupil size based on a convolution neural network (CNN). Usually, the shape of the pupil is not round, and 50% of pupils can be calculated using ellipses as the best fitting shapes. This paper uses the major and minor axes of an ellipse to represent the size of pupils and uses the two parameters as the output of the network. Regarding the input of the network, the dataset is in video format (continuous frames). Taking each frame from the videos and using these to train the CNN model may cause overfitting since the images are too similar. This study used data augmentation and calculated the structural similarity to ensure that the images had a certain degree of difference to avoid this problem. For optimizing the network structure, this study compared the mean error with changes in the depth of the network and the field of view (FOV) of the convolution filter. The result shows that both deepening the network and widening the FOV of the convolution filter can reduce the mean error. According to the results, the mean error of the pupil length is 5.437% and the pupil area is 10.57%. It can operate in low-cost mobile embedded systems at 35 frames per second, demonstrating that low-cost designs can be used for pupil size prediction.

FLEXOR: A support tool for efficient and seamless experiment data processing to evaluate musculo-articular stiffness

BACKGROUND AND OBJECTIVE

The evaluation of musculo-articular stiffness (MAS) is an increasingly demanded procedure with applications in different fields, such as sports performance and lower limbs injury prevention. However, this task is non-automated, time-consuming and error-prone due to manual handling of data streams and files across several software applications. Despite the fact that process automation of validated procedures helps to prevent errors, there is still a lack of easy-to-use tools for analysis, management and visualization of MAS trials.

METHODS

In the present work a tool called FLEXOR has been developed which applies mathematical methods and novel algorithms to automatically adjust curves of data streams for MAS analysis decreasing substantially time employed and errors. This tool permits to define different adjustment parameters, detect curve peaks and valleys, and display the results on the fly. FLEXOR has been implemented through a component-based software development (CBSD) process. All physiological fundamentals for the biomechanical measurement have been included in the tool developed. To describe the integration of all required components a 4 + 1 view model architecture has been used. The installation guide, the FLEXOR software and some data samples can be found on its GitHub repository (https://github.com/FlexorSoftware/flexor).

RESULTS

A multiplatform software tool to simplify traditional complex and manual procedures for MAS analysis is obtained. The tool turns them into a simple all-in-one procedure, reducing processing times from hours to a few minutes. The methodology was tested on multiple datasets generated by previous tools in former procedures as well as on real-time trials in the laboratory, showing identical results.

CONCLUSION

The results show that the developed tool can accomplish an unfilled essential task in the analysis, management and visualization of MAS measurement. The presented software tool empowers analysts to handle the different studies, investigate different parameters related to each experiment and even test with different output parameters in each experiment, enabling real-time trials and shared studies between different analysts.

Eyeing up the Future of the Pupillary Light Reflex in Neurodiagnostics

The pupillary light reflex (PLR) describes the constriction and subsequent dilation of the pupil in response to light as a result of the antagonistic actions of the iris sphincter and dilator muscles. Since these muscles are innervated by the parasympathetic and sympathetic nervous systems, respectively, different parameters of the PLR can be used as indicators for either sympathetic or parasympathetic modulation. Thus, the PLR provides an important metric of autonomic nervous system function that has been exploited for a wide range of clinical applications. Measurement of the PLR using dynamic pupillometry is now an established quantitative, non-invasive tool in assessment of traumatic head injuries. This review examines the more recent application of dynamic pupillometry as a diagnostic tool for a wide range of clinical conditions, varying from neurodegenerative disease to exposure to toxic chemicals, as well as its potential in the non-invasive diagnosis of infectious disease.

rPLR: an imaging system for measuring pupillary light reflex at a distance

Pupillary light reflex (PLR) is a simple noninvasive neurological test that can reveal a great amount of information of the neural system. We report here a novel imaging system for measuring PLR without using any restraints to limit the subject's movement. Our system incorporates a tracking component that can locate the subject's eye position and redirect the pupillary imaging component to follow the subject's movement. This system can measure PLR, at a distance from the subject, with high spatial resolution (<50  μm) and temporal resolution (120 Hz). Because this new PLR device can accommodate the subject's movement, it is well positioned to test in young children and other people who have difficulty remaining voluntarily still during tests.

System and measurement method for binocular pupillometry to study pupil size variability

BACKGROUND

An objective and noninvasive examination of pupil size variability can be used to assess the activity of the autonomous nervous system. We designed a system that enables binocular, fast, and accurate recordings of different types of pupil variabilities, which are synchronous with other biosignals. This type of measurement system is needed to extend the scope of pupillometry applications.

METHODS

In the proposed system, the left and right eyes are independently and interchangeably illuminated to generate alternating images, which are successively acquired by a single camera. The system is composed of four functional modules: the image acquisition module, the image processing unit, the light stimulator, and the controller. The proposed image processing algorithm approximates the shape of the pupil using the best-fit ellipse. The user control panel (controller) precisely sets the stimuli parameters and controls the entire measurement procedure.

RESULTS

The computer-based binocular system records the pupil size during the pupil light reflexes (direct and indirect) and spontaneous pupil size fluctuations, at a sampling rate up to 75 Hz, with a resolution better than 0.02 mm. Our initial laboratory tests confirmed that the new system is fast and precise (system accuracy better than 0.5% and repeatability better than 4%).

CONCLUSIONS

The proposed system's unique geometry and construction, and the method it uses to detect images from each eye, allows us to monitor the right and left eyes using a single camera with no overlap between the images. The system does not require a very experienced operator, because it is relatively simple and easy to use. Importantly, it is comfortable for the subjects. Additionally, the presented system can operate with other bio-measurement systems using a synchronous signal. These system capabilities can expand the scope of pupillometry research applications.

Analysis of parameters for the automatic computation of the tear film break-up time test based on CCLRU standards

Dry eye syndrome is affecting a remarkable percentage of population. The prevalence is 10-15% of normal population, and 18-30% of contact lenses users. The break-up time (BUT) is a clinical test used for the diagnosis of this disease. In this work, we perform an analysis of parameters for a global and a local automatic computation of the BUT measure, based on criteria of specificity and sensitivity. We have tested our methodology on a dataset composed of 18 videos annotated by 4 different experts. The local analysis preserves the results of the global approach providing useful additional information about the break-up tear zone.