Detecting abnormal behaviours of institutionalized older adults through a hybrid-inference approach

Soto-Mendoza, Valeria; García-Macías, J. Antonio; Chávez, Edgar; Gómez-Montalvo, Jorge R.; Quintana, Eduardo Martín

doi:10.1016/j.pmcj.2017.06.019

Cited by 8 publications

(2 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In intelligent environment settings, these techniques include but are not limited to the Hidden Markov Model (HMM), probabilistic hierarchical models of human behavior using HMM, conditional random fields, and dynamic Bayesian networks. Other works, such as those by [36] and [37], consider artificial neural networks for monitoring and predicting activities of daily living; On the other hand, [38] proposes a hybrid inference approach to detect abnormal user behavior, and [39] uses sequential pattern mining. Also, for the analysis of human behavior, [40] has proposed using a temporal structure or a set of actions over time with T-patterns, broadly used by [41] and [42] or sequential patterns through the GSP algorithm, proposed by Skirant [43].…”

Section: Behavior Analysismentioning

confidence: 99%

From Interaction Data to Personalized Learning: Mining User-Object Interactions in Intelligent Environments

Hernández-Calderón,

Benítez-Guerrero,

Rojano-Cáceres

et al. 2024

Proceedings of ISP RAS

View full text Add to dashboard Cite

The aim of this work is to contribute to the personalization of intelligent learning environments by analyzing user-object interaction data to identify On-Task and Off-Task behaviors. This is accomplished by monitoring and analyzing users' interactions while performing academic activities with a tangible-intangible hybrid system in a university intelligent environment configuration. With the proposal of a framework and the Orange Data Mining tool and the Neural Network, Random Forest, Naive Bayes, and Tree classification models, training and testing was carried out with the user-object interaction records of the 13 students (11 for training and two for testing) to identify representative sequences of behavior from user-object interaction records. The two models that had the best results, despite the small number of data, were the Neural Network and Naive Bayes. Although a more significant amount of data is necessary to perform a classification adequately, the process allowed exemplifying this process so that it can later be fully incorporated into an intelligent educational system to contribute to build personalized environments.

show abstract

Section: Behavior Analysismentioning

confidence: 99%

From Interaction Data to Personalized Learning: Mining User-Object Interactions in Intelligent Environments

Hernández-Calderón,

Benítez-Guerrero,

Rojano-Cáceres

et al. 2024

Proceedings of ISP RAS

View full text Add to dashboard Cite

show abstract

“…A fuzzy fusion process combines outputs for a final decision and alerts healthcare providers. In [100], a K-means model recognizes human activities, and a sequential pattern mining algorithm identifies the most frequent activity sequences for each user. To recognize abnormal behaviors, an ontology is employed to formally represent activities, and new activity sequences are compared to recognized frequent sequences using the Longest Common Subsequence (LCS) algorithm.…”

Section: Abnormal Human Behavior Recognitionmentioning

confidence: 99%

A hybrid and context-aware framework for normal and abnormal human behavior recognition

Mojarad,

Chibani,

Attal

et al. 2023

Soft Comput

View full text Add to dashboard Cite

Human behavior recognition is one of the significant components of Ambient Assisted Living (AAL) systems and personal assistive robots allowing to improve the quality of their lives in terms of safety, autonomy, and well-being. A critical aspect of preventing dangerous situations for users, especially elderlies, is to recognize abnormal human behavior. In spite of the extensive exploration of abnormality recognition in various fields, there remain some challenges in developing effective approaches for recognizing abnormal human behaviors in AAL systems due to the limitations of data-driven and knowledge-driven approaches. In this paper, a context-aware framework combining data-driven and knowledge-driven approaches is proposed to better characterize human behaviors and recognize abnormal behaviors using commonsense reasoning while considering human behavior context. The proposed framework comprises five main modules, which leverage Long Short-Term Memory (LSTM) models and Probabilistic Answer Set Programming (PASP)-based commonsense reasoning to recognize human activities and represent abnormal human behaviors, as well as reason about those behaviors. The proposed framework is evaluated using two datasets, namely Orange4Home and UCI HAR. The obtained results indicate the capability of the proposed framework to characterize human behaviors and recognize abnormal human behaviors with high performance.

show abstract

A Context-Aware Approach to Detect Abnormal Human Behaviors

Mojarad

Attal

Chibani

et al. 2021

Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track

View full text Add to dashboard Cite

Detecting abnormal behaviours of institutionalized older adults through a hybrid-inference approach

Cited by 8 publications

References 36 publications

From Interaction Data to Personalized Learning: Mining User-Object Interactions in Intelligent Environments

From Interaction Data to Personalized Learning: Mining User-Object Interactions in Intelligent Environments

A hybrid and context-aware framework for normal and abnormal human behavior recognition

A Context-Aware Approach to Detect Abnormal Human Behaviors

Contact Info

Product

Resources

About