Current research and the scarcity of mental health resources indicate that patients increasingly rely on internet-based health information. Occupational burnout and the associated stigma with the diagnosis often prevent people from seeking help for mental health issues. Digital health applications, including digital humans, have the potential to provide efficient and quality-focused healthcare while making health-related information more accessible. Digital humans can be described as digitally embodied conversational agents (ECA) and are becoming increasingly prevalent in healthcare, education, and retail. However, imitating a person in behavior remains one of the most challenging tasks. Building trust with patients is crucial for effective communication and decision-making. This thesis investigates how digital humans might leverage trust-building in a conversation to reduce the stigma associated with seeking help for occupational burnout. Expert interviews with psychologists, implementation of conversations with enhanced characteristics of benevolence, competence, and integrity, were conducted. Digital human interactions with six previous burnout patients were also interviewed and tested, where each interaction tested a digital human with enhanced characteristic of benevolence, competence and integrity. The study identified instrumental barriers as the primary obstacle, followed by attitudinal and stigma barriers. Qualitative analysis of the interviews revealed common themes that could hinder access to care such as not noticing the first symptoms of burnout due to being uninformed about the diagnosis and internal pressure to disappoint others. The interactions with various digital humans suggested a preference for those with similar conversational styles. Participants who identified themselves as action-driven exhibited a slight preference for competence in their interactions with digital humans. They believed that benevolence could be provided by their friends and family, highlighting the importance of competence in digital human support. Overall, the study revealed the importance of enhancing conversation competency and fostering a sense of security to build trust, especially for stigmatized patients. / Aktuell forskning och bristen på resurser för psykisk hälsa visar att patienter i allt högre grad förlitar sig på internet-baserad hälsoinformation. Utmattningssyndrom från arbetsplatsen och den stigmatisering förknippad med diagnosen hindrar ofta människor från att söka hjälp för psykiska problem. Digitala hälsotillämpningar, inklusive digital humans, har potential att tillhandahålla effektiv och kvalitetsfokuserad sjukvård vilket samtidigt möjliggör mer tillgänglig hälsorelaterad information. Digital humans kan beskrivas som digitalt förkroppsligade samtalsagenter och blir allt vanligare inom sjukvård, utbildning och detaljhandel. Att imitera en person i beteende är dock fortfarande en av de mest utmanande uppgifterna. Vidare är förtroendebyggande hos patienter avgörande för effektiv kommunikation och beslutsfattande. Denna avhandling undersöker hur digitala människor kan utnyttja förtroendeskapande i en konversation för att minska stigmat förknippat med att söka hjälp för yrkesmässig utmattning. Expertintervjuer med psykologer, implementerade konversationer med förbättrade egenskaper av välvilja, kompetens och integritet, genomfördes. Digital human interaktioner med sex tidigare utbrända patienter intervjuades och testades. Studien identifierade instrumentella barriärer som det främsta hindret, följt av attitydmässiga och stigmatiserande barriärer. Kvalitativ analys av intervjuerna avslöjade gemensamma teman som kan hindra tillgången till vård, t.ex. att inte lägga märke till de första symptomen på utbrändhet på grund av brist på tillgänglig information om diagnosen och inre press att göra andra besvikna. Interaktionerna med olika digitala människor tyder på en preferens för personer med liknande samtalsstil. Deltagare som identifierade sig som handlingsdrivna uppvisade en preferens för kompetens i sina interaktioner med digitala människor. De ansåg att välvilja kunde tillhandahållas av deras vänner och familj och belyste istället vikten av kompetens i digitalt mänskligt stöd. Sammantaget visade studien vikten av att förbättra visad kompetens i konversationer och främja en känsla av säkerhet för att bygga förtroende, särskilt för stigmatiserade patienter.

The last advancements in technology leads to an easy acquisition and spreading of multi-dimensional multimedia content, e.g. videos, which in many cases depict human faces. From such videos, valuable information describing the intrinsic characteristic of the recorded user can be retrieved: the features extracted from the facial patch are relevant descriptors that allow for the measurement of subject's emotional status or the identification of synthetic characters. One of the emerging challenges is the development of contactless approaches based on face analysis aiming at measuring the emotional status of the subject without placing sensors that limit or bias his experience. This raises even more interest in the context of Quality of Experience (QoE) measurement, or the measurement of user emotional status when subjected to a multimedia content, since it allows for retrieving the overall acceptability of the content as perceived by the end user. Measuring the impact of a given content to the user can have many implications from both the content producer and the end-user perspectives. For this reason, we pursue the QoE assessment of a user watching multimedia stimuli, i.e. 3D-movies, through the analysis of his facial features acquired by means of contactless approaches. More specifically, the user's Heart Rate (HR) was retrieved by using computer vision techniques applied to the facial recording of the subject and then analysed in order to compute the level of engagement. We show that the proposed framework is effective for long video sequences, being robust to facial movements and illumination changes. We validate it on a dataset of 64 sequences where users observe 3D movies selected to induce variations in users' emotional status. From one hand understanding the interaction between the user's perception of the content and his cognitive-emotional aspects leads to many opportunities to content producers, which may influence people's emotional statuses according to needs that can be driven by political, social, or business interests. On the other hand, the end-user must be aware of the authenticity of the content being watched: advancements in computer renderings allowed for the spreading of fake subjects in videos. Because of this, as a second challenge we target the identification of CG characters in videos by applying two different approaches. We firstly exploit the idea that fake characters do not present any pulse rate signal, while humans' pulse rate is expressed by a sinusoidal signal. The application of computer vision techniques on a facial video allows for the contactless estimation of the subject's HR, thus leading to the identification of signals that lack of a strong sinusoidality, which represent virtual humans. The proposed pipeline allows for a fully automated discrimination, validated on a dataset consisting of 104 videos. Secondly, we make use of facial spatio-temporal texture dynamics that reveal the artefacts introduced by computer renderings techniques when creating a manipulation, e.g. face swapping, on videos depicting human faces. To do so, we consider multiple temporal video segments on which we estimated multi-dimensional (spatial and temporal) texture features. A binary decision of the joint analysis of such features is applied to strengthen the classification accuracy. This is achieved through the use of Local Derivative Patterns on Three Orthogonal Planes (LDP-TOP). Experimental analyses on state-of-the-art datasets of manipulated videos show the discriminative power of such descriptors in separating real and manipulated sequences and identifying the creation method used. The main finding of this thesis is the relevance of facial features in describing intrinsic characteristics of humans. These can be used to retrieve significant information like the physiological response to multimedia stimuli or the authenticity of the human being itself. The application of the proposed approaches also on benchmark dataset returned good results, thus demonstrating real advancements in this research field. In addition to that, these methods can be extended to different practical application, from the autonomous driving safety checks to the identification of spoofing attacks, from the medical check-ups when doing sports to the users' engagement measurement when watching advertising. Because of this, we encourage further investigations in such direction, in order to improve the robustness of the methods, thus allowing for the application to increasingly challenging scenarios.