A Computer Vision Framework For Finger-Tapping Evaluation In Parkinson's Disease

Objective: To define and evaluate a Computer-Vision (CV) method for scoring Paced Finger-Tapping (PFT) in Parkinson's disease (PD) using quantitative motion analysis of index-fingers and to compare the obtained scores to the UPDRS (Unified Parkinson's Disease Rating Scale) finger-taps (FT). Background: The naked-eye evaluation of PFT in clinical practice results in coarse resolution to determine PD status. Besides, sensor mechanisms for PFT evaluation may cause patients discomfort. In order to avoid cost and effort of applying wearable sensors, a CV system for non-invasive PFT evaluation is introduced. Methods: A database of 221 PFT videos from 6 PD patients was processed. The subjects were instructed to position their hands above their shoulders besides the face and tap the index-finger against the thumb consistently with speed. They were facing towards a pivoted camera during recording. The videos were rated by two clinicians between symptom levels 0-to-3 using UPDRS-FT. The CV method incorporates a motion analyzer and a face detector. The method detects the face of testee in each video-frame. The frame is split into two images from face-rectangle center. Two regions of interest are located in each image to detect index-finger motion of left and right hands respectively. The tracking of opening and closing phases of dominant hand index-finger produces a tapping time-series. This time-series is normalized by the face height. The normalization calibrates the amplitude in tapping signal which is affected by the varying distance between camera and subject (farther the camera, lesser the amplitude). A total of 15 features were classified using K-nearest neighbor (KNN) classifier to characterize the symptoms levels in UPDRS-FT. The target ratings provided by the raters were averaged. Results: A 10-fold cross validation in KNN classified 221 videos between 3 symptom levels with 75% accuracy. An area under the receiver operating characteristic curves of 82.6% supports feasibility of the obtained features to replicate clinical assessments. Conclusions: The system is able to track index-finger motion to estimate tapping symptoms in PD. It has certain advantages compared to other technologies (e.g. magnetic sensors, accelerometers etc.) for PFT evaluation to improve and automate the ratings

Facial affect recognition and schizotypal personality characteristics

Aim: Deficits in facial affect recognition are well established in schizophrenia, yet relatively little research has examined facial affect recognition in hypothetically psychosis-prone or ‘schizotypal’ individuals. Those studies that have examined social cognition in psychosis-prone individuals have paid little attention to the association between facial emotion recognition and particular schizotypal personality features. The present study therefore sought to investigate relationships between facial emotion recognition and the different aspects of schizotypy.

Methods: Facial affect recognition accuracy was examined in 50 psychiatrically healthy individuals assessed for level of schizotypy using the Schizotypal Personality Questionnaire. This instrument provides a multidimensional measure of schizophrenia proneness, encompassing ‘cognitive-perceptual’, ‘interpersonal’ and ‘disorganized’ features of schizotypy. It was hypothesized that the cognitive-perceptual and interpersonal aspects of schizotypy would be associated with difficulties identifying facial expressions of emotion during a forced-choice recognition task using a standardized series of colour photographs.

Results: As predicted, interpersonal aspects of schizotypy (particularly social anxiety) were associated with reduced accuracy on the facial affect recognition task, but there was no association between affect recognition accuracy and cognitive-perceptual features of schizotypy.

Conclusions: These results suggest that subtle deficits in facial affect recognition in otherwise psychiatrically healthy individuals may be related to the vulnerability for interpersonal communication difficulties, as seen in schizophrenia.

Measuring and analyzing color and texture information in anatomical leaf cross sections: An approach using computer vision to aid plant species identification

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

COMT Met (158) modulates facial emotion recognition in bipolar I disorder mood episodes

Background: One of the many cognitive deficits reported in bipolar disorder (BD) patients is facial emotion recognition (FER), which has recently been associated with dopaminergic catabolism. Catechol-O-methyltransferase (COMT) is one of the main enzymes involved in the metabolic degradation of dopamine (DA) in the prefrontal cortex (PFC). The COMT gene polymorphism rs4680 (Val(158)Met) Met allele is associated with decreased activity of this enzyme in healthy controls. The objective of this study was to evaluate the influence of Val(158)Met on FER during manic and depressive episodes in BD patients and in healthy controls. Materials and methods: 64 BD type I patients (39 in manic and 25 in depressive episodes) and 75 healthy controls were genotyped for COMT rs4680 and assessed for FER using the Ekman 60 Faces (EK60) and Emotion Hexagon (Hx) tests. Results: Bipolar manic patients carrying the Met allele recognized fewer surprised faces, while depressed patients with the Met allele recognized fewer "angry" and "happy" faces. Healthy homozygous subjects with the Met allele had higher FER scores on the Hx total score, as well as on "disgust" and "angry" faces than other genotypes. Conclusion: This is the first study suggesting that COMT rs4680 modulates FER differently during BD episodes and in healthy controls. This provides evidence that PFC DA is part of the neurobiological mechanisms of social cognition. Further studies on other COMT polymorphisms that include euthymic BD patients are warranted. ClinicalTrials.gov Identifier: NCT00969. (C) 2011 Elsevier B.V. All rights reserved.

Swarm intelligence in computer vision: an application to object tracking

Tesis en inglés. Eliminadas las páginas en blanco del pdf

An evolutive approach for smile recognition in video sequences

Facial expression recognition is one of the most challenging research areas in the image recognition ¯eld and has been actively studied since the 70's. For instance, smile recognition has been studied due to the fact that it is considered an important facial expression in human communication, it is therefore likely useful for human–machine interaction. Moreover, if a smile can be detected and also its intensity estimated, it will raise the possibility of new applications in the future

Computer vision based eyewear selector

[EN]This paper describes a low-cost system that allows the user to visualize different glasses models in live video. The user can also move the glasses to adjust its position on the face. The system, which runs at 9.5 frames/s on general-purpose hardware, has a homeostatic module that keeps image parameters controlled. This is achieved by using a camera with motorized zoom, iris, white balance, etc. This feature can be specially useful in environments with changing illumination and shadows, like in an optical shop. The system also includes a face and eye detection module and a glasses management module.

A Food Recognition System for Diabetic Patients based on an Optimized Bag of Features Model

Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes a methodology for automatic food recognition, based on the Bag of Features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved in the BoF architecture, as well as the estimation of the corresponding parameters. For the design and evaluation of the prototype system, a visual dataset with nearly 5,000 food images was created and organized into 11 classes. The optimized system computes dense local features, using the scale-invariant feature transform on the HSV color space, builds a visual dictionary of 10,000 visual words by using the hierarchical k-means clustering and finally classifies the food images with a linear support vector machine classifier. The system achieved classification accuracy of the order of 78%, thus proving the feasibility of the proposed approach in a very challenging image dataset.

Computer Vision-Based Carbohydrate Estimation for Type 1 Diabetic Patients Using Smartphones

Background: Individuals with type 1 diabetes (T1D) have to count the carbohydrates (CHOs) of their meal to estimate the prandial insulin dose needed to compensate for the meal’s effect on blood glucose levels. CHO counting is very challenging but also crucial, since an error of 20 grams can substantially impair postprandial control. Method: The GoCARB system is a smartphone application designed to support T1D patients with CHO counting of nonpacked foods. In a typical scenario, the user places a reference card next to the dish and acquires 2 images with his/her smartphone. From these images, the plate is detected and the different food items on the plate are automatically segmented and recognized, while their 3D shape is reconstructed. Finally, the food volumes are calculated and the CHO content is estimated by combining the previous results and using the USDA nutritional database. Results: To evaluate the proposed system, a set of 24 multi-food dishes was used. For each dish, 3 pairs of images were taken and for each pair, the system was applied 4 times. The mean absolute percentage error in CHO estimation was 10 ± 12%, which led to a mean absolute error of 6 ± 8 CHO grams for normal-sized dishes. Conclusion: The laboratory experiments demonstrated the feasibility of the GoCARB prototype system since the error was below the initial goal of 20 grams. However, further improvements and evaluation are needed prior launching a system able to meet the inter- and intracultural eating habits.