Several researchers have proposed methods and designed systems for the automatic recognition of activities and abnormal behaviors with the goal of early detecting cognitive impairment. In this paper, we propose LOTAR, a hybrid behavioral analysis system coupling state of the art machine learning techniques with knowledge-based and data mining methods. Medical models designed in collaboration with cognitive neuroscience researchers guide the recognition of short- and long-term abnormal behaviors. In particular, we focus on historical behavior analysis for long-term anomaly detection, which is the principal novelty with respect to our previous works. We present preliminary results obtained by evaluating the method on a dataset acquired during three months of experimentation in a real patient's home. Results indicate the potential utility of the system for long-term monitoring of cognitive health.

Analysis of long-term abnormal behaviors for early detection of cognitive decline

RIBONI, DANIELE;
2016

Abstract

Several researchers have proposed methods and designed systems for the automatic recognition of activities and abnormal behaviors with the goal of early detecting cognitive impairment. In this paper, we propose LOTAR, a hybrid behavioral analysis system coupling state of the art machine learning techniques with knowledge-based and data mining methods. Medical models designed in collaboration with cognitive neuroscience researchers guide the recognition of short- and long-term abnormal behaviors. In particular, we focus on historical behavior analysis for long-term anomaly detection, which is the principal novelty with respect to our previous works. We present preliminary results obtained by evaluating the method on a dataset acquired during three months of experimentation in a real patient's home. Results indicate the potential utility of the system for long-term monitoring of cognitive health.
9781509019410
Computer science applications; Computer vision and pattern recognition; Computer networks and communications; Human-computer interaction
File in questo prodotto:
File Dimensione Formato  
16-pasta.pdf

Solo gestori archivio

Tipologia: versione pre-print
Dimensione 201.18 kB
Formato Adobe PDF
201.18 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11584/196834
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 5
  • ???jsp.display-item.citation.isi??? 2
social impact