TY - CHAP
T1 - Human behavioral anomaly pattern mining within an IoT environment
T2 - An exploratory study
AU - Sánchez-García, Rosario
AU - Dominguez-Rodriguez, Alejandro
AU - Ocegueda-Miramontes, Violeta
AU - Aguilar, Leocundo
AU - Rodríguez-Díaz, Antonio
AU - Cervera-Torres, Sergio
AU - Sanchez, Mauricio A.
N1 - Publisher Copyright:
© 2024 Elsevier Inc.
PY - 2024/1
Y1 - 2024/1
N2 - 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.
AB - 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.
KW - 2023 OA procedure
KW - Internet of things
KW - Machine learning
KW - Outlier detection
KW - Pattern mining
KW - Behavioral patterns
UR - http://www.scopus.com/inward/record.url?scp=85176114232&partnerID=8YFLogxK
U2 - 10.1016/bs.adcom.2023.10.003
DO - 10.1016/bs.adcom.2023.10.003
M3 - Chapter
AN - SCOPUS:85176114232
SN - 9780323910897
VL - 133
T3 - Advances in Computers
SP - 33
EP - 57
BT - Internet of Things
A2 - Marques, Gonçalo
PB - Elsevier
ER -