Monitoramento ubíquo da saúde mental: detectando padrões de sociabilidade enriquecidos por contexto através do processamento de eventos complexos.

• Ano de defesa: 2020
• Autor(a) principal: MOURA, Ivan Rodrigues de
• Orientador(a): SILVA, Francisco José da Silva e
• Banca de defesa: SILVA, Francisco José da Silva e , COUTINHO, Luciano Reis , SOARES NETO, Carlos de Salles , CAMARGO, Raphael Yokoingawa de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Maranhão
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM CIÊNCIA DA COMPUTAÇÃO/CCET
• Departamento: DEPARTAMENTO DE INFORMÁTICA/CCET
• País: Brasil
• Palavras-chave em Português: saúde mental; transtornos mentais; computação ubíqua; computação móvel; padrões de sociabilidade;
• Palavras-chave em Inglês: mental health; mental disorders; jubiquitous computing; mobile computing; sociability pattern.
• Área do conhecimento CNPq: Ciência da Computação; Ciências da Saúde
• Link de acesso: https://tedebc.ufma.br/jspui/handle/tede/4145

Traditionally, the monitoring of individuals with mental disorders is conducted through face-to-face meetings with professionals specialized in mental health. Today, however, computational methods can use ubiquitous devices (e.g., smartphones and wearable technologies) to monitor social behaviors related to mental health rather than relying on self-reports. These devices represent a valuable source of contextual data that allows the identification of the social activities experienced by individuals in their daily routine. Therefore, the use of these technologies to identify social activities habit enables the recognition of abnormal social behaviors that may be mental disorders indicative. For this reason, this study presents a new approach to monitoring mental health through social situation awareness. This work introduces an algorithm capable of detecting sociability patterns, i.e., it characterizes the periods of the day when the individual socializes habitually. The recognition of social routine is performed under different context conditions (e.g., workday and weekend), which allows differentiating abnormal social behaviors from changes in social habits expected in certain situations. The solution presented is also able to identify changes in social patterns that may be indicative of mental disorders presence. The implementation of the proposed algorithm used the combination of the Frequent Patterns Mining approach with the Complex Events Processing, which allows the realization of the continuous social data stream processing. The evaluation performed demonstrated that context-based recognition provides a better understanding of the social routine, also indicating that the proposed solution is capable of detecting sociability patterns similar to a batch algorithm. Additionally, it was validated the performance of the social behavior change detection mechanism.