Recognition of activities of daily living (ADLs) performed in smart homes proved to be very effective when the interaction of the inhabitant with household items is considered. Analyzing how objects are manipulated can be particularly useful, in combination with other sensor data, to detect anomalies in performing ADLs, and hence to support early diagnosis of cognitive impairments for elderly people. Recent improvements in sensing technologies can overcome several limitations of the existing techniques to detect object manipulations, often based on RFID, wearable sensors and/or computer vision methods. In this work we propose an unobtrusive solution which shifts all the monitoring burden at the objects side. In particular, we investigate the effectiveness of using tiny BLE beacons equipped with accelerometer and temperature sensors attached to everyday objects. We adopt statistical methods to analyze in realtime the accelerometer data coming from the objects, with the purpose of detecting specific manipulations performed by seniors in their homes. We describe our technique and we present the preliminary results obtained by evaluating the method on a real dataset. The results indicate the potential utility of the method in enriching ADLs and abnormal behaviors recognition systems, by providing detailed information about object manipulations.
Let the objects tell what you are doing / G. Civitarese, C. Bettini, S. Belfiore - In: UbiComp '16 Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct[s.l] : ACM, 2016. - ISBN 9781450344623. - pp. 773-782 (( convegno Joint Conference on Pervasive and Ubiquitous Computing tenutosi a Heidelberg nel 2016 [10.1145/2968219.2968285].
Let the objects tell what you are doing
G. CivitaresePrimo
;C. BettiniSecondo
;
2016
Abstract
Recognition of activities of daily living (ADLs) performed in smart homes proved to be very effective when the interaction of the inhabitant with household items is considered. Analyzing how objects are manipulated can be particularly useful, in combination with other sensor data, to detect anomalies in performing ADLs, and hence to support early diagnosis of cognitive impairments for elderly people. Recent improvements in sensing technologies can overcome several limitations of the existing techniques to detect object manipulations, often based on RFID, wearable sensors and/or computer vision methods. In this work we propose an unobtrusive solution which shifts all the monitoring burden at the objects side. In particular, we investigate the effectiveness of using tiny BLE beacons equipped with accelerometer and temperature sensors attached to everyday objects. We adopt statistical methods to analyze in realtime the accelerometer data coming from the objects, with the purpose of detecting specific manipulations performed by seniors in their homes. We describe our technique and we present the preliminary results obtained by evaluating the method on a real dataset. The results indicate the potential utility of the method in enriching ADLs and abnormal behaviors recognition systems, by providing detailed information about object manipulations.File | Dimensione | Formato | |
---|---|---|---|
2016-Hasca-UbicompWS.pdf
accesso riservato
Tipologia:
Post-print, accepted manuscript ecc. (versione accettata dall'editore)
Dimensione
6.75 MB
Formato
Adobe PDF
|
6.75 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.