This paper presents an application of an AI-based sensors network characterized by Passive Infrared (PIR) and door contact sensors for remote home monitoring of activities of daily living (ADLs) on ageing people during the epidemic event caused by the SARS-CoV-2 virus. The scope of the work is to demonstrate the relevance in the combined use of artificial intelligence (AI) and sensor networks on the measurement of living behavior of ageing people. To this scope, an AI-based sensor network has been installed in the living environment of three Italian ageing users diagnosed with early dementia and collected data have been used to assess the effect of the confinement during the lockdown period on the ADLs variation. The main ADLs (toileting, eating, going outside, sleep and location change) measured through the AI-based sensor network have been analysed on a time period of 2 months before the lockdown (January and February 2020) and 2 months during the lockdown (March and April 2020). Analysis has been performed considering the mean and standard deviation of the measured occurrences on a daily basis, before and during the lockdown. A Student t-test has been computed to evaluate the significance of the reported changes on the ADLs, demonstrating that there are statistically significant differences between the two observed periods for most of the considered ADLs.

AI-based sensor network for ADLs monitoring on ageing people during COVID-19 epidemic

Casaccia S.
;
Revel G. M.;Scalise L.
2021-01-01

Abstract

This paper presents an application of an AI-based sensors network characterized by Passive Infrared (PIR) and door contact sensors for remote home monitoring of activities of daily living (ADLs) on ageing people during the epidemic event caused by the SARS-CoV-2 virus. The scope of the work is to demonstrate the relevance in the combined use of artificial intelligence (AI) and sensor networks on the measurement of living behavior of ageing people. To this scope, an AI-based sensor network has been installed in the living environment of three Italian ageing users diagnosed with early dementia and collected data have been used to assess the effect of the confinement during the lockdown period on the ADLs variation. The main ADLs (toileting, eating, going outside, sleep and location change) measured through the AI-based sensor network have been analysed on a time period of 2 months before the lockdown (January and February 2020) and 2 months during the lockdown (March and April 2020). Analysis has been performed considering the mean and standard deviation of the measured occurrences on a daily basis, before and during the lockdown. A Student t-test has been computed to evaluate the significance of the reported changes on the ADLs, demonstrating that there are statistically significant differences between the two observed periods for most of the considered ADLs.
978-1-6654-1980-2
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/291650
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