The objective of this work is to define a measurement protocol to compute and distinguish abnormal from normal behavior of older people with early to middle stage dementia living alone at home using training artificial intelligence (AI) algorithms (in specific K-means, Agglomerative and Spectral Clustering Algorithms). Unlabeled activities of daily living (ADLs) databases were acquired from an AI-based sensor network composed of three Passive Infrared (PIR) motion sensors and two door sensors which are installed in voluntary participants’ houses in Italy, Netherlands, Switzerland and Norway. Our blind approach proposes an unsupervised learning-based solution, representing real-world use case where it is not possible to provide accurate labeling and annotation of the sensor data. Results indicate two out of three algorithms, which applied for 32 participants during 75 days of recording, are valid to create clustering groups for participants with acceptable Silhouette coefficient of 0.77 and 0.65 for K-means and Agglomerative clustering, respectively, and 0.13 for Spectral clustering method that makes this algorithm less reliable respect to others.

Assessment of normal and abnormal behaviour of people with dementia in living environment through non-invasive sensors and unsupervised AI

Farsi A.;Casaccia S.
;
Revel G. M.
2022

Abstract

The objective of this work is to define a measurement protocol to compute and distinguish abnormal from normal behavior of older people with early to middle stage dementia living alone at home using training artificial intelligence (AI) algorithms (in specific K-means, Agglomerative and Spectral Clustering Algorithms). Unlabeled activities of daily living (ADLs) databases were acquired from an AI-based sensor network composed of three Passive Infrared (PIR) motion sensors and two door sensors which are installed in voluntary participants’ houses in Italy, Netherlands, Switzerland and Norway. Our blind approach proposes an unsupervised learning-based solution, representing real-world use case where it is not possible to provide accurate labeling and annotation of the sensor data. Results indicate two out of three algorithms, which applied for 32 participants during 75 days of recording, are valid to create clustering groups for participants with acceptable Silhouette coefficient of 0.77 and 0.65 for K-means and Agglomerative clustering, respectively, and 0.13 for Spectral clustering method that makes this algorithm less reliable respect to others.
978-1-6654-0893-6
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11566/305501
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact