Human behavioral anomaly pattern mining within an IoT environment: An exploratory study

Rosario Sánchez-García, Alejandro Dominguez-Rodriguez, Violeta Ocegueda-Miramontes, Leocundo Aguilar, Antonio Rodríguez-Díaz, Sergio Cervera-Torres, Mauricio A. Sanchez

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

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A Psychological assessment is fundamental for the detection of clinical mental disorders. However, standard psychometric tools such as questionnaires, which are the gold standard for assessing clinical disorders, face important drawbacks regarding subjective bias. Accordingly, new methods and technologies are coming to the fore to complement psychometric assessments so that more consistent and replicable assessments can potentiate patient-focused diagnosis accuracy. In this sense, the development of Internet of Things (IoT) networks has been part of the technological advances that are characteristic of Industry 4.0, due to the large amount of information provided by networked sensors regarding the environment and the interaction of individuals in it, allowing the detection of behavioral patterns exercised. This paper proposes a data analysis of human behavioral patterns from a connected home environment. The potentials of pattern mining techniques are investigated for detecting behavioral anomalies within such patterns. Results show that detecting anomalies within human behavioral patterns is possible. We argue that with such promising results, a system could be potentially applied under contexts such as suicide prevention or discovering other undiagnosed mental disorders that individuals may present throughout their life.

Original languageEnglish
Title of host publicationAdvances in Computers
Publication statusE-pub ahead of print/First online - 7 Nov 2023

Publication series

NameAdvances in Computers
ISSN (Print)0065-2458


  • Behavioral patterns
  • Internet of things
  • Machine learning
  • Outlier detection
  • Pattern mining
  • 2023 OA procedure


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