*Article* **Recognizing Context-Aware Human Sociability Patterns Using Pervasive Monitoring for Supporting Mental Health Professionals**

**Ivan Rodrigues de Moura 1,†,\*, Ariel Soares Teles 1,2, Markus Endler 3, Luciano Reis Coutinho 1 and Francisco José da Silva e Silva 1**


† This paper is an extended version of our paper published in I. Rodrigues de Moura, F. José da Silva e Silva, L. Reis Coutinho and A. Soares Teles, "Mental Health Ubiquitous Monitoring: Detecting Context-Enriched Sociability Patterns Through Complex Event Processing", in Proceedings of the 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), Rochester, MN, USA, 28–30 July 2020; pp. 239–244, doi:10.1109/CBMS49503.2020.00052.

**Abstract:** Traditionally, mental health specialists monitor their patients' social behavior by applying subjective self-report questionnaires in face-to-face meetings. Usually, the application of the selfreport questionnaire is limited by cognitive biases (e.g., memory bias and social desirability). As an alternative, we present a solution to detect context-aware sociability patterns and behavioral changes based on social situations inferred from ubiquitous device data. This solution does not focus on the diagnosis of mental states, but works on identifying situations of interest to specialized professionals. The proposed solution consists of an algorithm based on frequent pattern mining and complex event processing to detect periods of the day in which the individual usually socializes. Social routine recognition is performed under different context conditions to differentiate abnormal social behaviors from the variation of usual social habits. The proposed solution also can detect abnormal behavior and routine changes. This solution uses fuzzy logic to model the knowledge of the mental health specialist necessary to identify the occurrence of behavioral change. Evaluation results show that the prediction performance of the identified context-aware sociability patterns has strong positive relation (Pearson's correlation coefficient >70%) with individuals' social routine. Finally, the evaluation conducted recognized that the proposed solution leading to the identification of abnormal social behaviors and social routine changes consistently.

**Keywords:** mental health; pervasive computing; context awareness; sociability; social behavior; sociability pattern
