Global demographics show a steady growth in the population of cognitively impaired patients. Consequently, the aging societies are looking to adopt smart technologies in healthcare services to early detect the onset of cognitive decline. These technologies include advanced methods that enable continuous in-house monitoring of the elderly's activities through unobtrusive sensing for recognizing abnormal behaviors that may indicate cognitive deficits. In an earlier work, we proposed a technique to detect the early symptoms of cognitive impairment by continuously monitoring the daily behavior of an elderly at home to recognize fine-grained abnormal behaviors. Recognition was based on rule-based descriptions of anomalies manually defined by domain experts. However, those rules strongly depend on the specific home environment, on the used sensors, and on the particular habits of the elderly; hence, their definition is time-expensive, and rules are not seamlessly portable to different environments. In order to address this issue, in this paper we propose a method to automatically learn the rulebased definitions of behavioral anomalies. In particular, we use a rule induction algorithm to infer those rules based on a dataset of activities and anomalies. We evaluated our method using a dataset of activities and abnormal behaviors carried out in an instrumented smart home. Our method achieves high precision and recall values, around 0.97 and 0.85, respectively, which are comparable to those obtained using manually-defined rules.

Towards automatic induction of abnormal behavioral patterns for recognizing mild cognitive impairment

RIBONI, DANIELE;
2016

Abstract

Global demographics show a steady growth in the population of cognitively impaired patients. Consequently, the aging societies are looking to adopt smart technologies in healthcare services to early detect the onset of cognitive decline. These technologies include advanced methods that enable continuous in-house monitoring of the elderly's activities through unobtrusive sensing for recognizing abnormal behaviors that may indicate cognitive deficits. In an earlier work, we proposed a technique to detect the early symptoms of cognitive impairment by continuously monitoring the daily behavior of an elderly at home to recognize fine-grained abnormal behaviors. Recognition was based on rule-based descriptions of anomalies manually defined by domain experts. However, those rules strongly depend on the specific home environment, on the used sensors, and on the particular habits of the elderly; hence, their definition is time-expensive, and rules are not seamlessly portable to different environments. In order to address this issue, in this paper we propose a method to automatically learn the rulebased definitions of behavioral anomalies. In particular, we use a rule induction algorithm to infer those rules based on a dataset of activities and anomalies. We evaluated our method using a dataset of activities and abnormal behaviors carried out in an instrumented smart home. Our method achieves high precision and recall values, around 0.97 and 0.85, respectively, which are comparable to those obtained using manually-defined rules.
9781450337397
Machine learning; Mild cognitive impairment; Recognition of abnormal behaviors; Rule induction; Telecare; Software
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11584/196848
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