Multimodaalsel emotsioonide tuvastamisel põhineva inimese-roboti suhtluse arendamine

Väitekirja elektrooniline versioon ei sisalda publikatsiooneÜks afektiivse arvutiteaduse peamistest huviobjektidest on mitmemodaalne emotsioonituvastus, mis leiab rakendust peamiselt inimese-arvuti interaktsioonis. Emotsiooni äratundmiseks uuritakse nendes süsteemides nii inimese näoilmeid kui kakõnet. Käesolevas töös uuritakse inimese emotsioonide ja nende avaldumise visuaalseid ja akustilisi tunnuseid, et töötada välja automaatne multimodaalne emotsioonituvastussüsteem. Kõnest arvutatakse mel-sageduse kepstri kordajad, helisignaali erinevate komponentide energiad ja prosoodilised näitajad. Näoilmeteanalüüsimiseks kasutatakse kahte erinevat strateegiat. Esiteks arvutatakse inimesenäo tähtsamate punktide vahelised erinevad geomeetrilised suhted. Teiseks võetakse emotsionaalse sisuga video kokku vähendatud hulgaks põhikaadriteks, misantakse sisendiks konvolutsioonilisele tehisnärvivõrgule emotsioonide visuaalsekseristamiseks. Kolme klassifitseerija väljunditest (1 akustiline, 2 visuaalset) koostatakse uus kogum tunnuseid, mida kasutatakse õppimiseks süsteemi viimasesetapis. Loodud süsteemi katsetati SAVEE, Poola ja Serbia emotsionaalse kõneandmebaaside, eNTERFACE’05 ja RML andmebaaside peal. Saadud tulemusednäitavad, et võrreldes olemasolevatega võimaldab käesoleva töö raames loodudsüsteem suuremat täpsust emotsioonide äratundmisel. Lisaks anname käesolevastöös ülevaate kirjanduses väljapakutud süsteemidest, millel on võimekus tunda äraemotsiooniga seotud ̆zeste. Selle ülevaate eesmärgiks on hõlbustada uute uurimissuundade leidmist, mis aitaksid lisada töö raames loodud süsteemile ̆zestipõhiseemotsioonituvastuse võimekuse, et veelgi enam tõsta süsteemi emotsioonide äratundmise täpsust.Automatic multimodal emotion recognition is a fundamental subject of interest in affective computing. Its main applications are in human-computer interaction. The systems developed for the foregoing purpose consider combinations of different modalities, based on vocal and visual cues. This thesis takes the foregoing modalities into account, in order to develop an automatic multimodal emotion recognition system. More specifically, it takes advantage of the information extracted from speech and face signals. From speech signals, Mel-frequency cepstral coefficients, filter-bank energies and prosodic features are extracted. Moreover, two different strategies are considered for analyzing the facial data. First, facial landmarks' geometric relations, i.e. distances and angles, are computed. Second, we summarize each emotional video into a reduced set of key-frames. Then they are taught to visually discriminate between the emotions. In order to do so, a convolutional neural network is applied to the key-frames summarizing the videos. Afterward, the output confidence values of all the classifiers from both of the modalities are used to define a new feature space. Lastly, the latter values are learned for the final emotion label prediction, in a late fusion. The experiments are conducted on the SAVEE, Polish, Serbian, eNTERFACE'05 and RML datasets. The results show significant performance improvements by the proposed system in comparison to the existing alternatives, defining the current state-of-the-art on all the datasets. Additionally, we provide a review of emotional body gesture recognition systems proposed in the literature. The aim of the foregoing part is to help figure out possible future research directions for enhancing the performance of the proposed system. More clearly, we imply that incorporating data representing gestures, which constitute another major component of the visual modality, can result in a more efficient framework

Evaluating of psychiatric behavior in obese children and adolescents

Abstract IntroductionObesity is a medical condition  that it may have a harmful effect on health, leading to increased illness and reduced life expectancy. This study is aimed to evaluate the relationship of psychiatry disorders in overweight and obese children and adolescents.MethodsIn this was case-control study, one hundred and sixty child and Adolescent were recruited. The sampling method of this study was non-probability and biased. Study instruments were SDQ, CDI, STAI, Peds QL. All questionnaires were self-administrating that was completed by subjects or their parents. Differences between groups were examined using t-test and chi-square tests as appropriate. ResultsThe results our study showed no significant different in scores of anxiety between two groups. But showed significant different in scores of depression, quality of life, and strength and difficult between two groups.  Also there was no significant difference in gender effect on anxiety and Depression. However, in Quality of life test showed that emotional symptoms were more in girl than boys. In contrast, the conduct problems were more in boys than girls. Anxiety and Depression was more in adolescents than childrenConcussion Our study showed obesity has a negative effect on the anxiety, depression, and self-esteem of children and adolescents. It can be suggested that obesity might be a more important risk factor for depression, anxiety, and other psychiatry disorders. This study also emphasizes the importance of prevention of obesity

Survey on Emotional Body Gesture Recognition

Automatic emotion recognition has become a trending research topic in the past decade. While works based on facial expressions or speech abound, recognizing affect from body gestures remains a less explored topic. We present a new comprehensive survey hoping to boost research in the field. We first introduce emotional body gestures as a component of what is commonly known as "body language" and comment general aspects as gender differences and culture dependence. We then define a complete framework for automatic emotional body gesture recognition. We introduce person detection and comment static and dynamic body pose estimation methods both in RGB and 3D. We then comment the recent literature related to representation learning and emotion recognition from images of emotionally expressive gestures. We also discuss multi-modal approaches that combine speech or face with body gestures for improved emotion recognition. While pre-processing methodologies (e.g., human detection and pose estimation) are nowadays mature technologies fully developed for robust large scale analysis, we show that for emotion recognition the quantity of labelled data is scarce. There is no agreement on clearly defined output spaces and the representations are shallow and largely based on naive geometrical representations

The Prevalence of Mental Health Problems and the Associated Familial Factors in Adolescents in the South of Iran

Background Mental health problems are common among adolescents. Proper screening and rehabilitation could improve adolescents’ function at the present time and in the future. This study aimed to assess the prevalence of psychiatric disorders and the associated familial factors among high school students. Materials and Methods The present cross-sectional study was conducted on 630 high school students (315 boys and 315 girls) aged 13-17 years in Jahrom, Iran. The participants were selected using random cluster sampling. The data were collected using the self-report version of strength and difficulties questionnaire (SDQ), and were analyzed using the SPSS statistical software, version 16.0. Results: The results showed that 22.38% of the students had total difficulty (14.9% of boys, and 29.8% of girls). The highest prevalence was related to peer relationship problems (23%) followed by conduct problems (18.1%), hyperactivity (11.1%), pro-social behaviors (6.3%), and emotional problems (5.7%). The results of multivariate logistic regression analysis revealed that female gender (odds ratio [OR]: 2.52, 95% confidence interval [CI]: 1.68-3.66) increased the odds; while grade 9 (OR=0.52, 95% CI: 0.32-0.83), and number of siblings (OR: 0.88, 95% CI: 0.78-0.99) decreased the odds of mental health problems (

Indicator bacteria community in seawater and coastal sediment: the Persian Gulf as a case.

Abstract BACKGROUND: The aim of present work was to assess the concentration levels as well as vertical distribution of indicator bacteria including total coliform, fecal coliform, Pseudomonas aeruginosa, and Heterotrophic Plate Count (HPC) in the marine environment (seawater and coastal sediments) and evaluate the correlation between indicator bacteria and some physicochemical parameters of surface sediments as well as seawaters. METHODS: A total number of 48 seawater and sediment samples were taken from 8 stations (each site 6 times with an interval time of 2 weeks) between June and September 2014. Seawater and sediment samples were collected from 30 cm under the surface samples and different sediment depths (0, 4, 7, 10, 15, and 20 cm) respectively, along the Persian Gulf in Bushehr coastal areas. RESULTS: Based on the results, the average numbers of bacterial indicators including total coliform, fecal coliform, and Pseudomonas aeruginosa as well as HPC in seawater samples were 1238.13, 150.87, 8.22 MPN/100 ml and 1742.91 CFU/ml, respectively, and in sediment samples at different depths (from 0-20 cm) varied between 25 × 103 to 51.67 × 103, 5.63 × 103 to 12.46 × 103, 17.33 to 65 MPN/100 ml, 36 × 103 to 147.5 × 103 CFU/ml, respectively. There were no statistically significant relationships between the indicator organism concentration levels with temperature as well as pH value of seawater. A reverse correlation was found between the level of indicator bacteria and salinity of seawater samples. Also results revealed that the sediment texture influenced abundance of indicators bacteria in sediments. As the concentration levels of indicators bacteria were higher in muddy sediments compare with sandy ones. CONCLUSION: Result conducted Bushehr coastal sediments constitute a reservoir of indicator bacteria, therefore, whole of the indicators determined were distinguished to be present in higher levels in sediments than in the overlying seawater. It was concluded that the concentration levels of microbial indicators decreased with depth in sediments. Except total coliform, the numbers of other bacteria including fecal coliform, Pseudomonas aeruginosa and HPC bacteria significantly declined in the depth between 10 and 15 cm