With a growing population of elderly people the number of subjects at risk of cognitive disorders is rapidly increasing. Many research groups are studying pervasive solutions to continuously and unobtrusively monitor fragile subjects at their homes, reducing health-care costs and supporting medical diagnosis. Among the several behavioral aspects which clinicians are interested in monitoring, anomalous behaviors while performing activities of daily living are of great importance. In this work, we aim at improving the state of the art on this topic by enabling the recognition of fine-grained anomalies by detecting specific object manipulations. We attach tiny Bluetooth Low Energy accelerometers to several household objects in order to detect which manipulations are performed by the inhabitant on which object. Detected manipulations, combined with data from other environmental sensors deployed in the home, are used to infer ADLs and fine-grained abnormal behaviors. Preliminary results on a dataset with hundreds of complex activities captured in a smarthome environment show the effectiveness of the proposed method.

Monitoring objects manipulations to detect abnormal behaviors / G. Civitarese, C. Bettini - In: Pervasive Computing and Communications Workshops (PerCom Workshops), 2017 IEEE International Conference on[s.l] : IEEE, 2017. - ISBN 9781509043385. - pp. 388-393 (( convegno Pervasive Computing and Communications Workshops (PerCom Workshops) tenutosi a Kona nel 2017.

Monitoring objects manipulations to detect abnormal behaviors

G. Civitarese
Primo
;
C. Bettini
Ultimo
2017

Abstract

With a growing population of elderly people the number of subjects at risk of cognitive disorders is rapidly increasing. Many research groups are studying pervasive solutions to continuously and unobtrusively monitor fragile subjects at their homes, reducing health-care costs and supporting medical diagnosis. Among the several behavioral aspects which clinicians are interested in monitoring, anomalous behaviors while performing activities of daily living are of great importance. In this work, we aim at improving the state of the art on this topic by enabling the recognition of fine-grained anomalies by detecting specific object manipulations. We attach tiny Bluetooth Low Energy accelerometers to several household objects in order to detect which manipulations are performed by the inhabitant on which object. Detected manipulations, combined with data from other environmental sensors deployed in the home, are used to infer ADLs and fine-grained abnormal behaviors. Preliminary results on a dataset with hundreds of complex activities captured in a smarthome environment show the effectiveness of the proposed method.
Settore INF/01 - Informatica
2017
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/496477
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