4 resultados para automatic affect analysis


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This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.

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BACKGROUND Persons with schizophrenia and related disorders may be particularly sensitive to a number of determinants of service use, including those related with illness, socio-demographic characteristics and organizational factors. The objective of this study is to identify factors associated with outpatient contacts at community mental health services of patients with schizophrenia or related disorders. METHODS This cross-sectional study analyzed 1097 patients. The main outcome measure was the total number of outpatient consultations during one year. Independent variables were related to socio-demographic, clinical and use of service factors. Data were collected from clinical records. RESULTS The multilevel linear regression model explained 46.35% of the variance. Patients with significantly more contacts with ambulatory services were not working and were receiving welfare benefits (p = 0.02), had no formal education (p = 0.02), had a global level of severity of two or three (four being the most severe) (p < 0.001), with one or more inpatient admissions (p < 0.001), and in contact with both types of professional (nurses and psychiatrists) (p < 0.001). The patients with the fewest ambulatory contacts were those with diagnoses of persistent delusional disorders (p = 0.04) and those who were attended by four of the 13 psychiatrists (p < 0.001). CONCLUSIONS As expected, the variables that explained the use of community service could be viewed as proxies for severity of illness. The most surprising finding, however, was that a group of four psychiatrists was also independently associated with use of ambulatory services by patients with schizophrenia or related disorders. More research is needed to carefully examine how professional support networks interact to affect use of mental health.

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BACKGROUND Lower limb amputees exhibit postural control deficits during standing which can affect their walking ability. OBJECTIVES The primary purpose of the present study was to analyze the thorax, pelvis, and hip kinematics and the hip internal moment in the frontal plane during gait in subjects with Unilateral Transtibial Amputation (UTA). METHOD The participants included 25 people with UTA and 25 non-amputees as control subjects. Gait analysis was performed using the Vicon(r) Motion System. We analyzed the motion of the thorax, pelvis, and hip (kinematics) as well as the hip internal moment in the frontal plane. RESULTS The second peak of the hip abductor moment was significantly lower on the prosthetic side than on the sound side (p=.01) and the control side (right: p=.01; left: p=.01). During middle stance, the opposite side of the pelvis was higher on the prosthetic side compared to the control side (right: p=.01: left: p=.01). CONCLUSIONS The joint internal moment at the hip in the frontal plane was lower on the prosthetic side than on the sound side or the control side. Thorax and pelvis kinematics were altered during the stance phase on the prosthetic side, presumably because there are mechanisms which affect postural control during walking.

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This paper addresses a fully automatic landmarks detection method for breast reconstruction aesthetic assessment. The set of landmarks detected are the supraesternal notch (SSN), armpits, nipples, and inframammary fold (IMF). These landmarks are commonly used in order to perform anthropometric measurements for aesthetic assessment. The methodological approach is based on both illumination and morphological analysis. The proposed method has been tested with 21 images. A good overall performance is observed, although several improvements must be achieved in order to refine the detection of nipples and SSNs.