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 / D. Riboni, G. Civitarese, C. Bettini - In: Pervasive Computing and Communication Workshops (PerCom Workshops), 2016 IEEE International Conference on[s.l] : IEEE, 2016. - ISBN 9781509019410. - pp. 1-6 (( Intervento presentato al 13. convegno Conference on Pervasive Computing and Communication Workshops (PerCom Workshops) tenutosi a Sydney nel 2016 [10.1109/PERCOMW.2016.7457139].
Analysis of long-term abnormal behaviors for early detection of cognitive decline
G. CivitareseSecondo
;C. BettiniUltimo
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.File | Dimensione | Formato | |
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