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.
['Gabriele Civitarese', 'Stefano Belfiore', 'Claudio Bettini']
Let the objects tell what you are doing