Captación De Potenciales Eléctricos Oculares Para Su Uso Viable En Interfaz Hombre-Máquina

La captación de señales oculares es útil para el control en interfaz hombre-máquina (IHM) en pacientes que sufren de enfermedades motoras incapacitantes (e.g. Esclerosis lateral amiotrófica, o infortunados eventos que dejan al paciente con una severa discapacidad como un derrame cerebral o accidentes vehiculares) de manera tal que puedan usar el movimiento ocular como sistema de control en interfaz hombre-máquina para el mejoramiento de la calidad de vida de estos pacientes. Para ello, esta investigación tiene por objetivo desarrollar un dispositivo de bajo costo para la captación de señales oculares que permita ser empleado en IHMs. Materiales y métodos: Se diseñó un dispositivo que fue validado en una población de diez estudiantes adultos de la Facultad de Ingeniería Biomédica de la Universidad Especializada de las Américas (Panamá) y se registraron los potenciales del movimiento ocular vertical y horizontal. Resultados: Se confirma que la magnitud de la señal es directamente proporcional al desplazamiento ocular y que no hay correlación estadística significativa entre los potenciales para una distancia de desplazamiento ocular específica, es decir, tanto el ojo izquierdo como el derecho registraron el mismo potencial para las mismas distancias. El potencial mínimo registrado fue de 88,0±16,8 mV y el máximo de 382,0 ± 72,08mV. El dispositivo se vinculó a una bombilla de intensidad lumínica variable de manera tal que, dependiendo del potencial registrado, la intensidad de la bombilla varía. Esto demuestra que el dispositivo puede ser usado como interfaz en una IHM. Conclusión: El análisis estadístico de la investigación brinda confiabilidad a los resultados captados por el dispositivo diseñado (captador de señales mioeléctricas). Al encender el led se muestra la posibilidad de que el dispositivo sea empleado para interfaz hombre-máquina. The capture of ocular signals is useful for controlling human-machine interfaces in patients suffering from disabling motor diseases (e.g., amyotrophic lateral sclerosis, or unfortunate events, such as accidents, that leave the patient with a severe disability). Such interfaces aim to improve their quality of life. Thus, this research aims to develop a low-cost device for capturing eye signals that allow it to be used in HMIs. Materials and methods: A device was designed and validated in a population of 10 adult students from the Faculty of Biomedical Engineering of the Specialized University of the Americas (Panama), and the potential of vertical and horizontal eye movement were recorded. Results: The magnitude of the signal is directly proportional to the ocular displacement. Furthermore, there is no significant statistical difference among the recorded potential for a specific ocular displacement; that is, both the left and the right eye marked the same potential for same displaced distances. The minimum potential recorded was 88.0 ± 16.8 mV, and the maximum was 382.0 ± 72.08mV. The device was connected to a variable intensity light bulb, and depending on the registered potential, the intensity of the light changed. Thus, the device could be used as an interface in an HMI. Conclusion: The statistical analysis showed the reliability of the designed device (myoelectric signal collector). When the LED is switched on, the possibility of the equipment being used for humanmachine interfaces is shown

Adversarial Attacks on Classifiers for Eye-based User Modelling

An ever-growing body of work has demonstrated the rich information content available in eye movements for user modelling, e.g. for predicting users' activities, cognitive processes, or even personality traits. We show that state-of-the-art classifiers for eye-based user modelling are highly vulnerable to adversarial examples: small artificial perturbations in gaze input that can dramatically change a classifier's predictions. We generate these adversarial examples using the Fast Gradient Sign Method (FGSM) that linearises the gradient to find suitable perturbations. On the sample task of eye-based document type recognition we study the success of different adversarial attack scenarios: with and without knowledge about classifier gradients (white-box vs. black-box) as well as with and without targeting the attack to a specific class, In addition, we demonstrate the feasibility of defending against adversarial attacks by adding adversarial examples to a classifier's training data.Comment: 9 pages, 7 figure

Sensory System for Implementing a Human—Computer Interface Based on Electrooculography

This paper describes a sensory system for implementing a human–computer interface based on electrooculography. An acquisition system captures electrooculograms and transmits them via the ZigBee protocol. The data acquired are analysed in real time using a microcontroller-based platform running the Linux operating system. The continuous wavelet transform and neural network are used to process and analyse the signals to obtain highly reliable results in real time. To enhance system usability, the graphical interface is projected onto special eyewear, which is also used to position the signal-capturing electrodes

Gaze Assisted Prediction of Task Difficulty Level and User Activities in an Intelligent Tutoring System (ITS)

Efforts toward modernizing education are emphasizing the adoption of Intelligent Tutoring Systems (ITS) to complement conventional teaching methodologies. Intelligent tutoring systems empower instructors to make teaching more engaging by providing a platform to tutor, deliver learning material, and to assess students’ progress. Despite the advantages, existing intelligent tutoring systems do not automatically assess how students engage in problem solving? How do they perceive various activities, while solving a problem? and How much time they spend on each discrete activity leading to the solution? In this research, we present an eye tracking framework that can assess how eye movements manifest students’ perceived activities and overall engagement in a sketch based Intelligent tutoring system, “Mechanix.” Mechanix guides students in solving truss problems by supporting user initiated feedback. Through an evaluation involving 21 participants, we show the potential of leveraging eye movement data to recognize students’ perceived activities, “reading, gazing at an image, and problem solving,” with an accuracy of 97.12%. We are also able to leverage the user gaze data to classify problems being solved by students as difficult, medium, or hard with an accuracy of more than 80%. In this process, we also identify the key features of eye movement data, and discuss how and why these features vary across different activities

Gaze Assisted Prediction of Task Difficulty Level and User Activities in an Intelligent Tutoring System (ITS)

Efforts toward modernizing education are emphasizing the adoption of Intelligent Tutoring Systems (ITS) to complement conventional teaching methodologies. Intelligent tutoring systems empower instructors to make teaching more engaging by providing a platform to tutor, deliver learning material, and to assess students’ progress. Despite the advantages, existing intelligent tutoring systems do not automatically assess how students engage in problem solving? How do they perceive various activities, while solving a problem? and How much time they spend on each discrete activity leading to the solution? In this research, we present an eye tracking framework that can assess how eye movements manifest students’ perceived activities and overall engagement in a sketch based Intelligent tutoring system, “Mechanix.” Mechanix guides students in solving truss problems by supporting user initiated feedback. Through an evaluation involving 21 participants, we show the potential of leveraging eye movement data to recognize students’ perceived activities, “reading, gazing at an image, and problem solving,” with an accuracy of 97.12%. We are also able to leverage the user gaze data to classify problems being solved by students as difficult, medium, or hard with an accuracy of more than 80%. In this process, we also identify the key features of eye movement data, and discuss how and why these features vary across different activities

Analisi del Movimento nel riconoscimento delle Emozioni nel Morbo di Parkinson

Il morbo di Parkinson è una malattia neurodegenerativa che si manifesta con disturbi legati principalmente al movimento, alcuni dei quali sono la rigidità, il tremore e la bradicinesia. Un sintomo particolare che deriva da quest'ultima è l'ipomimia, ovvero la compromissione dei muscoli facciali, la quale influisce sull'espressione delle emozioni e sul loro riconoscimento. Sono stati presentati i principali strumenti e task che vengono utilizzati per studiare l'ipomimia, tramite i quali si è potuto dimostrare una notevole carenza di espressività nei soggetti affetti da Parkinson rispetto a controlli sani. Risulta decisamente importante studiare questo sintomo per poter predisporre un percorso riabilitativo adatto, poiché l'ipomimia influenza la qualità di vita dei soggetti affetti da tale sintomo