With the progressive aging of the population, monitoring the state of frailty of a person becomes increasingly important to prevent risk factors, which can lead to loss of autonomy and to hospitalization. Hygiene care, in particular, represents a wake-up call to detect a decline in physical and mental well-being. With the assistance of both environmental and localized sensors, measurements of hygiene-related activities can be made quickly and consistently over time. We here propose to remotely monitor these activities using a fixed camera and deep learning algorithms. In particular, three activities are considered, i.e., washing face, brushing teeth and arranging hair, together with the non-Action class. Considering a dataset consisting of 11 healthy subjects of different age and sex, we show that using a Long-Short Term Memory (LSTM) neural network the selected activities can be distinguished with an accuracy of more than 92%, thus proving the validity of the proposed approach.
A Deep Learning Approach to Remotely Monitor People's Frailty Status / Senigagliesi, L.; Nocera, A.; Angelini, M.; De Grazia, D.; Ciattaglia, G.; Olivieri, F.; Rippo, M. R.; Gambi, E.. - 2023-:(2023), pp. 1-4. (Intervento presentato al convegno 28th IEEE Symposium on Computers and Communications, ISCC 2023 tenutosi a tun nel 2023) [10.1109/ISCC58397.2023.10218270].
A Deep Learning Approach to Remotely Monitor People's Frailty Status
Senigagliesi L.
;Nocera A.;Angelini M.;Ciattaglia G.;Olivieri F.;Rippo M. R.;Gambi E.
2023-01-01
Abstract
With the progressive aging of the population, monitoring the state of frailty of a person becomes increasingly important to prevent risk factors, which can lead to loss of autonomy and to hospitalization. Hygiene care, in particular, represents a wake-up call to detect a decline in physical and mental well-being. With the assistance of both environmental and localized sensors, measurements of hygiene-related activities can be made quickly and consistently over time. We here propose to remotely monitor these activities using a fixed camera and deep learning algorithms. In particular, three activities are considered, i.e., washing face, brushing teeth and arranging hair, together with the non-Action class. Considering a dataset consisting of 11 healthy subjects of different age and sex, we show that using a Long-Short Term Memory (LSTM) neural network the selected activities can be distinguished with an accuracy of more than 92%, thus proving the validity of the proposed approach.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
